CN109980700A - A kind of distributed generation resource multi-objection optimization planning method, apparatus and equipment - Google Patents

A kind of distributed generation resource multi-objection optimization planning method, apparatus and equipment Download PDF

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CN109980700A
CN109980700A CN201910280915.5A CN201910280915A CN109980700A CN 109980700 A CN109980700 A CN 109980700A CN 201910280915 A CN201910280915 A CN 201910280915A CN 109980700 A CN109980700 A CN 109980700A
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distributed generation
generation resource
load
node
power
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CN109980700B (en
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吴亚雄
唐俊熙
曹华珍
高崇
何璇
李�浩
王天霖
陈沛东
张俊潇
李阳
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Ltd Of Guangdong Power Grid Developmental Research Institute
Guangdong Power Grid Co Ltd
Power Grid Program Research Center of Guangdong Power Grid Co Ltd
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Ltd Of Guangdong Power Grid Developmental Research Institute
Guangdong Power Grid Co Ltd
Power Grid Program Research Center of Guangdong Power Grid Co Ltd
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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
    • 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
    • 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]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

This application discloses a kind of distributed generation resource multi-objection optimization planning methods, device and equipment, the generated output fluctuation of distributed generation resource is considered first, establish the distributed generation resource power output probabilistic model of the active power output probability correlation about wind-power electricity generation and photovoltaic power generation, simultaneously, consider that the load growth of distributed generation resource is uncertain, establish the distributed generation resource load probabilistic distribution model determined by node load base power and power increment, then the object of planning function of distributed generation resource is constructed according to target requirement, according to the trend constraint condition of distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model foundation object of planning function, finally recycle the dragonfly algorithm based on multiple-objection optimization, fast and effeciently find out the optimal solution for meeting termination condition, obtain distributed generation resource optimum programming scheme.

Description

A kind of distributed generation resource multi-objection optimization planning method, apparatus and equipment
Technical field
This application involves ENERGY PLANNING technical field more particularly to a kind of distributed generation resource multi-objection optimization planning method, Device and equipment.
Background technique
With the fast development of renewable energy power generation technology, a large amount of wind-powered electricity generations and photovoltaic distributed plant-grid connection power grid, Influence to electric power netting safe running and scheduling is increasing.Important supplement of the distributed generation resource as the garden great Dian has cleaning, height The characteristics of effect, will become me and cross one of the important measures for promoting energy-saving and emission-reduction and coping with climate change.
Shared specific gravity is increasing in the power system for distributed generation resource, this proposes traditional Power System Planning New challenge and requirement.The distributed generation resource of photovoltaic power generation and wind-power electricity generation, due to the variation of weather, power generation throughout the year goes out There are bigger fluctuations for power situation, cause the generated output of distributed generation resource to distribute and serious unevenness occur, influence power train The operation stability of system and the reliability of ENERGY PLANNING；Simultaneously as the load class of subscriber of distributed generation resource is different, industry is negative Lotus, Commercial Load and resident load etc., load growth equally exist biggish uncertainty, lead to the electricity of distributed generation resource Power distribution is difficult to balance, and equally influences the operation stability of electric system and the reliability of ENERGY PLANNING；It does not mention also at present The generated output fluctuation and the probabilistic solution of load growth of distributed generation resource can be well solved out.
Summary of the invention
The embodiment of the present application provides a kind of distributed generation resource multi-objection optimization planning method, apparatus and equipment, examines simultaneously Consider the fluctuation of distributed generation resource generated output and the uncertainty of load growth, fast and effeciently solves the more of distributed generation resource Goal Programming Problem.
In view of this, the application first aspect provides a kind of distributed generation resource multi-objection optimization planning method, including with Lower step:
101, building distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model；
102, the object of planning function for constructing the distributed generation resource, establishes the constraint condition of the object of planning function, The constraint condition includes: trend constraint condition, node voltage constraint condition and distributed generation resource injecting power constraint condition, institute Trend constraint condition is stated according to distributed generation resource power output probabilistic model and the distributed generation resource load probabilistic distribution model It establishes；
103, it is optimized according to object of planning function of the dragonfly algorithm to the distributed generation resource, output, which meets, to change For the optimal solution of termination condition, distributed generation resource optimum programming scheme is obtained；
Wherein, distributed generation resource power output probabilistic model include distributed generation resource wind-power electricity generation active power output probability and Photovoltaic power generation active power output probability, the distributed generation resource load probabilistic distribution model include the active power and nothing of node load Function power.
Preferably, before step 101, further includes:
100, according to the history intensity of illumination data and historical wind speed data of distributed generation resource, determine that photovoltaic power generation is active out Power probability and wind-power electricity generation active power output probability.
Preferably, step 103 specifically includes:
1031, dragonfly algorithm parameter and initialization of population；
1032, the initial fitness function value of dragonfly individual in population is calculated；
1033, the object of planning function is optimized according to the 5 of dragonfly algorithm kinds of behaviors, is constantly iterated to calculate, more The position of new dragonfly individual；
1034, output iteration reaches corresponding maximum adaptation degree functional value and dragonfly individual when default maximum number of iterations, Obtain distributed generation resource optimum programming scheme.
Preferably, the construction method of the distributed generation resource load probabilistic distribution model are as follows:
1011, the historical load data of the node load of distributed generation resource is obtained；
1012, the historical load data is clustered according to K-Mediods clustering methodology, is determined in initial clustering The heart；
1013, the Euclidean distance for calculating the initial cluster center and each historical load data, according to described initial Cluster centre and the Euclidean distance complete the clustering of each historical load data, obtain cluster result；
1014, the corresponding historical load data of each cluster result is fitted according to cluster result, is determined The classification growth factor of each type load；
1015, according to the basic load power of each node load and the corresponding classification growth factor, building is distributed Power supply load probabilistic distribution model.
Preferably, the object of planning function specifically:
Min F=λ1CDG+λ2CLoss；
Wherein, F is total cost target, λ1、λ2For the weight of sub-goal, CDGFor distributed generation resource investment cost, r is load equipment yearly depreciation, ciIt is installed for node i single The expense of bit capacity distributed generation resource, SDGiThe capacity of distributed generation resource, C are installed for node iLossFor system losses cost, CpsFor For unit sales rate of electricity, PlLossFor the active loss of the l articles branch, TmaxHourage, N is lost for annual peak loadlIt is total for system Circuitry number.
Preferably, the trend constraint condition specifically:
Wherein, PGiFor the active power of node i generator injection, QGiFor the reactive power of node i generator injection, PDGi It is the active power of node i access distributed generation resource injection, ViFor the node voltage of node i, VjFor the node voltage of node j, GijThe feeder line conductance between node i and j, BijThe feeder line susceptance between node i and j, PLDi,tLoad for node i t is active Power, θijPhase angle difference between node i and j, QLDi,tFor the reactive load power of node i t, ai,tFor node i t Load growth coefficient, PLDi,t-1For the load active power of node i t-1, QLDi,t-1For node i t-1 load without Function power.
The application second aspect additionally provides a kind of distributed generation resource multi-objection optimization planning device, comprising:
Modeling module, for constructing distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model；
Planning module establishes the object of planning function for constructing the object of planning function of the distributed generation resource Constraint condition, the constraint condition include: trend constraint condition, node voltage constraint condition and distributed generation resource injecting power about Beam condition, the trend constraint condition is according to distributed generation resource power output probabilistic model and the distributed generation resource Load Probability Distributed model is established；
Optimization module, for being optimized according to object of planning function of the dragonfly algorithm to the distributed generation resource, Output meets the optimal solution of stopping criterion for iteration, obtains distributed generation resource optimum programming scheme；
Wherein, distributed generation resource power output probabilistic model include distributed generation resource wind-power electricity generation active power output probability and Photovoltaic power generation active power output probability, the distributed generation resource load probabilistic distribution model include the active power and nothing of node load Function power.
Preferably, further includes:
Preprocessing module determines light for the history intensity of illumination data and historical wind speed data according to distributed generation resource Volt power generation active power output probability and wind-power electricity generation active power output probability.
Preferably, the optimization module specifically includes:
Initialization submodule is used for dragonfly algorithm parameter and initialization of population；
Fitness submodule, for calculating the initial fitness function value of dragonfly individual in population；
Submodule is updated, for optimizing according to 5 kinds of behaviors of dragonfly algorithm to the object of planning function, constantly repeatedly In generation, calculates, and updates the position of dragonfly individual；
Output sub-module, for export when iteration reaches default maximum number of iterations corresponding maximum adaptation degree functional value and Dragonfly individual, obtains distributed generation resource optimum programming scheme.
The application third aspect additionally provides a kind of distributed generation resource multi-objection optimization planning equipment, and the equipment includes place Manage device and memory；
Said program code is transferred to the processor for storing program code by the memory；
The processor is more for the distributed generation resource according to the instruction execution first aspect in said program code Objective optimization planing method.
As can be seen from the above technical solutions, the embodiment of the present application has the advantage that
In the application, a kind of distributed generation resource multi-objection optimization planning method is provided, comprising: 101, building distributed electrical Source power output probabilistic model and distributed generation resource load probabilistic distribution model；102, the object of planning function of distributed generation resource is constructed, The constraint condition of object of planning function is established, constraint condition includes: trend constraint condition, node voltage constraint condition and distribution Power supply injecting power constraint condition, trend constraint condition are general according to distributed generation resource power output probabilistic model and distributed generation resource load Rate distributed model is established；103, it is optimized according to object of planning function of the dragonfly algorithm to distributed generation resource, output is full The optimal solution of sufficient stopping criterion for iteration obtains distributed generation resource optimum programming scheme；Wherein, distributed generation resource power output probabilistic model Wind-power electricity generation active power output probability and photovoltaic power generation active power output probability including distributed generation resource, distributed generation resource Load Probability Distributed model includes the active power and reactive power of node load.
Distributed generation resource multi-objection optimization planning method provided by the present application considers the generated output of distributed generation resource first Fluctuation establishes the distributed generation resource power output probability mould of the active power output probability correlation about wind-power electricity generation and photovoltaic power generation Type, meanwhile, consider that the load growth of distributed generation resource is uncertain, establishes by node load base power and power increment Then the distributed generation resource load probabilistic distribution model of decision constructs the object of planning letter of distributed generation resource according to target requirement Number, according to the tide of distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model foundation object of planning function Constraint condition is flowed, the dragonfly algorithm based on multiple-objection optimization is finally recycled, fast and effeciently finds out and meet termination condition most Excellent solution obtains distributed generation resource optimum programming scheme.Therefore, distributed generation resource multi-objection optimization planning side provided by the present application Method can consider that the generated output fluctuation of distributed generation resource and load growth are uncertain simultaneously, be with object of planning function Optimization aim carries out multiple-objection optimization to object of planning function using dragonfly algorithm and seeks optimal solution, is a kind of quickly and effectively solution It makes rational planning for the method for distributed generation resource.
Detailed description of the invention
Fig. 1 is that a kind of process of one embodiment of distributed generation resource multi-objection optimization planning method provided by the present application is shown It is intended to；
Fig. 2 is a kind of process of another embodiment of distributed generation resource multi-objection optimization planning method provided by the present application Schematic diagram；
Fig. 3 is that a kind of structure of one embodiment of distributed generation resource multi-objection optimization planning device provided by the present application is shown It is intended to；
Fig. 4 is the flow diagram of the dragonfly algorithm provided in the embodiment of the present application.
Specific embodiment
In order to make those skilled in the art more fully understand application scheme, below in conjunction in the embodiment of the present application Attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only this Apply for a part of the embodiment, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art exist Every other embodiment obtained under the premise of creative work is not made, shall fall in the protection scope of this application.
In order to make it easy to understand, referring to Fig. 1, a kind of distributed generation resource multi-objection optimization planning method provided by the present application One embodiment, comprising:
Step 101, building distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model；
Wherein, distributed generation resource power output probabilistic model includes the wind-power electricity generation active power output probability and photovoltaic of distributed generation resource Generate electricity active power output probability, and distributed generation resource load probabilistic distribution model includes the active power and reactive power of node load.
It should be noted that in the embodiment of the present application, to consider that generated output fluctuation and the load of distributed generation resource increase It is long uncertain, establish respectively distributed generation resource corresponding with generated output fluctuation contribute probabilistic model and with load growth not The corresponding distributed generation resource load probabilistic distribution model of certainty.The generated output probability of distributed generation resource includes that photovoltaic power generation goes out Power and wind power generation output, since distributed generation resource accesses power grid by gird-connected inverter, gird-connected inverter adjusts its output Alternating current and the frequency of alternating current are consistent with phase, and most electric energy can be presented as active output at this time, and therefore, the application is real It applies and only considers active power output in example, do not consider idle power output.The expression formula of distributed generation resource power output probabilistic model are as follows:
PDG=PPV+PWG；
Wherein, PDGFor distributed generation resource power output, PPVFor distributed photovoltaic power generation active power output, PWGFor distributed wind hair Electric active power output.
The active power output P of distributed photovoltaic power generationPVAre as follows:
Wherein, PPVNFor photovoltaic power generation capacity (rated output power), ENLight when for generated output being rated output power According to intensity, E is intensity of illumination.Intensity of illumination E approximation submits to two-parameter Beta distribution, probability density function are as follows:
Wherein, EmFor the maximum value of intensity of illumination, α, β are the form parameter of Beta distribution, and Γ (x) is about variable x's Integral function is represented by
Distributed wind-power generator active power output PWGAre as follows:
Wherein, PWGNFor the rated output power of blower, vNFor rated wind speed, vinTo cut wind speed, voffFor cut-out wind speed, The general Follow Weibull Distribution of wind speed v, probability density function are as follows:
Wherein, k and c is respectively the form parameter and scale parameter of Weibull distribution, can pass through the equal of historical wind speed data Value and standard deviation approximation obtain.
Consider the uncertainty of distributed generation resource load growth, distributed generation resource load is generally divided into industrial load, business Load and resident load three classes, corresponding classification growth factor can be arranged in each load type, according to the classification determined The basic load power of growth factor and node load, the probability Distribution Model that can establish electric load are as follows:
Wherein, PLDi,tFor the load active power of node i t, QLDi,tFor the reactive load power of node i t, ai,tFor the load growth coefficient of node i t, PLDi,t-1For the load active power of node i t-1, QLDi,t-1For node i The reactive load power of t-1.
Step 102, the object of planning function for constructing distributed generation resource, establish the constraint condition of object of planning function, constrain Condition includes: trend constraint condition, node voltage constraint condition and distributed generation resource injecting power constraint condition, trend constraint item Part is according to distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model foundation.
Further, object of planning function specifically:
Min F=λ1CDG+λ2CLoss；
Wherein, F is total cost target, λ1、λ2For the weight of sub-goal, CDGFor distributed generation resource investment cost, r is load equipment yearly depreciation, ciIt is installed for node i single The expense of bit capacity distributed generation resource, SDGiThe capacity of distributed generation resource, C are installed for node iLossFor system losses cost, CpsFor For unit sales rate of electricity, PlLossFor the active loss of the l articles branch, TmaxHourage, N is lost for annual peak loadlIt is total for system Circuitry number.
Further, trend constraint condition specifically:
Wherein, PGiFor the active power of node i generator injection, QGiFor the reactive power of node i generator injection, PDGi It is the active power of node i access distributed generation resource injection, ViFor the node voltage of node i, VjFor the node voltage of node j, GijThe feeder line conductance between node i and j, BijThe feeder line susceptance between node i and j, wherein PLDi,tFor the negative of node i t Lotus active power, θijPhase angle difference between node i and j, QLDi,tFor the reactive load power of node i t, ai,tFor node The load growth coefficient of i t, PLDi,t-1For the load active power of node i t-1, QLDi,t-1For node i t-1's Reactive load power.
It should be noted that, according to electric system ENERGY PLANNING demand, making programme in the embodiment of the present application Objective function, and determine its constraint condition followed.The constraint condition of the object of planning function of distributed generation resource include trend about Beam condition, node voltage constraint condition and distributed generation resource injecting power constraint condition, trend constraint condition therein can basis Model obtained in step 101 and its variable are formulated.
Wherein, object of planning function is with the minimum target of total cost, it may be assumed that
Min F=λ1CDG+λ2CLoss；
In formula, F is total cost target, λ1、λ2For the weight of sub-goal, CDGFor distributed generation resource investment cost, r is load equipment yearly depreciation, ciFor node i installation The expense of unit capacity distributed generation resource, SDGiThe capacity of distributed generation resource, C are installed for node iLossFor system losses cost, Cps For for unit sales rate of electricity, PlLossFor the active loss of the l articles branch, TmaxHourage, N is lost for annual peak loadlFor system Total circuitry number.
Trend constraint condition are as follows:
In formula, PGi、QGiFor the active power and reactive power of the injection of node i generator, PDGiIt is that node i access is distributed The active power (active power output for only considering distributed generation resource) of power supply injection, Vi、VjFor the node voltage of node i and j, Gij、 BijFeeder line conductance and susceptance, P between node i and jLDi、QLDiIt is the load power of node i, θijPhase between node i and j Angular difference, PLDi,tFor the load active power of node i t, QLDi,tFor the reactive load power of node i t.
Consider load growth variation, have:
In formula, PLDi,tFor the load active power of node i t, QLDi,tFor the reactive load power of node i t, ai,tFor the load growth coefficient of node i t, PLDi,t-1For the load active power of node i t-1, QLDi,t-1For node i The reactive load power of t-1.
Node voltage constraint condition are as follows:
Vimin≤Vi≤Vi,max, i=1,2 ..., n；
Distributed generation resource injecting power constraint condition are as follows:
PDGimin≤PDGi≤PDGimax, i=1,2 ..., n.
Step 103 is optimized according to object of planning function of the dragonfly algorithm to distributed generation resource, and output, which meets, to change For the optimal solution of termination condition, distributed generation resource optimum programming scheme is obtained.
It should be noted that dragonfly algorithm (Dragonfly Algorithm) is a kind of multi-objective optimization algorithm, by group All possible factors (separation, alignment, cohesion, food attraction and natural enemy repulsive force) of body behavior are all taken into account, can It is enough quickly to be restrained near optimal value, and have good global optimizing ability and stability.By all of distributed generation resource The number of iterations and convergence item of algorithm is arranged in the dragonfly population of node photovoltaic power generation capacity and wind-power electricity generation capacity as algorithm Part calculates the fitness function of dragonfly individual, carries out optimizing to dragonfly individual, updates the position of dragonfly individual, and output obtains excellent Changing the convergence result calculated is optimum programming scheme.
The distributed generation resource multi-objection optimization planning method provided in the embodiment of the present application considers distributed generation resource first Generated output fluctuation establishes the distributed generation resource power output of the active power output probability correlation about wind-power electricity generation and photovoltaic power generation Probabilistic model, meanwhile, consider that the load growth of distributed generation resource is uncertain, establishes by node load base power and power Then the distributed generation resource load probabilistic distribution model that increasing value determines constructs the planning mesh of distributed generation resource according to target requirement Scalar functions, according to distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model foundation object of planning function Trend constraint condition, finally recycle the dragonfly algorithm based on multiple-objection optimization, fast and effeciently find out and meet termination condition Optimal solution, obtain distributed generation resource optimum programming scheme.Therefore, distributed generation resource multi-objection optimization planning provided by the present application Method can consider that the generated output fluctuation of distributed generation resource and load growth are uncertain, simultaneously with object of planning function For optimization aim, multiple-objection optimization is carried out to object of planning function using dragonfly algorithm and seeks optimal solution, is a kind of quickly and effectively solution It certainly makes rational planning for the method for distributed generation resource.
In order to make it easy to understand, referring to Fig. 2, a kind of distributed generation resource multi-objection optimization planning method provided by the present application Another embodiment, comprising:
Step 201, history intensity of illumination data and historical wind speed data according to distributed generation resource, determine that photovoltaic power generation has Function power output probability and wind-power electricity generation active power output probability.
It should be noted that the photovoltaic power generation power output of distributed generation resource is related with the intensity of illumination data of distributed generation resource, Wind power generation output is related with air speed data, for the uncertainty of consideration photovoltaic power generation and the active power output of wind-power electricity generation, this Shen Please be in embodiment, acquisition history intensity of illumination data and historical wind speed data first, according to history intensity of illumination data and history The relationship of air speed data and generated output establishes distributed generation resource power output probabilistic model.
The active power output P of distributed photovoltaic power generationPVAre as follows:
Wherein, PPVNFor photovoltaic power generation capacity (rated output power), ENLight when for generated output being rated output power According to intensity, E is intensity of illumination.Intensity of illumination E approximation submits to two-parameter Beta distribution, probability density function are as follows:
Wherein, EmFor the maximum value of intensity of illumination, α, β are the form parameter of Beta distribution, and Γ (x) is about variable x's Integral function is represented by
Distributed wind-power generator active power output PWGAre as follows:
Wherein, PWGNFor the rated output power of blower, vNFor rated wind speed, vinTo cut wind speed, voffFor cut-out wind speed, The general Follow Weibull Distribution of wind speed v, probability density function are as follows:
Wherein, k and c is respectively the form parameter and scale parameter of Weibull distribution, can pass through the equal of historical wind speed data Value and standard deviation approximation obtain.
Step 202, building distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model.
It should be noted that the step 202 in the embodiment of the present application is consistent with the step 101 in a upper embodiment, herein No longer it is described in detail.
Further, the construction method of distributed generation resource load probabilistic distribution model are as follows:
2011, the historical load data of the node load of distributed generation resource is obtained；
2012, historical load data is clustered according to K-Mediods clustering methodology, determines initial cluster center；
2013, the Euclidean distance for calculating institute beginning cluster centre and each historical load data, according to initial cluster center and Europe Family name's distance completes the clustering of each historical load data, obtains cluster result；
2014, the corresponding historical load data of each cluster result is fitted according to cluster result, determines each type load Classification growth factor；
2015, according to the load power of each node and corresponding classification growth factor, distributed generation resource Load Probability is constructed Distributed model.
It should be noted that the load growth uncertainty of distributed generation resource can be by clustering in the embodiment of the present application Determine growing direction, the load data sample of 24 hours n load bus of history day, table are collected in the interval as unit of hour It is shown as:
Wherein, i indicates i-th of load bus.
Clustering processing is carried out to data sample using K-Mediods clustering method, determines initial cluster center.Due to Load is generally divided into industrial load, Commercial Load and resident load three classes, therefore cluster centre is that quantity is selected as 3.The application The K-Mediods clustering method of use is smaller to the susceptibility of noise, not will cause division result deviation for outlier Excessive, a small number of data not will cause significant impact.
The Euclidean distance of each data sample and center of a sample is calculated, preliminary load bus group is carried out and divides, is more than repetition Step no longer changes up to cluster result, that is, completes load characteristics clustering division.It is equal to a node load after obtaining cluster result Can determine affiliated classification, thus may determine that its classify growth factor, according to the corresponding classification growth factor of each node load with Basic load power can construct distributed generation resource load probabilistic distribution model.
Step 203, the object of planning function for constructing distributed generation resource, establish the constraint condition of object of planning function, constrain Condition includes: trend constraint condition, node voltage constraint condition and distributed generation resource injecting power constraint condition, trend constraint item Part is according to distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model foundation.
Further, object of planning function specifically:
Min F=λ1CDG+λ2CLoss；
Wherein, F is total cost target, λ1、λ2For the weight of sub-goal, CDGFor distributed generation resource investment cost, r is load equipment yearly depreciation, ciIt is installed for node i single The expense of bit capacity distributed generation resource, SDGiThe capacity of distributed generation resource, C are installed for node iLossFor system losses cost, CpsFor For unit sales rate of electricity, PlLossFor the active loss of the l articles branch, TmaxHourage, N is lost for annual peak loadlIt is total for system Circuitry number.
Further, trend constraint condition specifically:
Wherein, PGiFor the active power of node i generator injection, QGiFor the reactive power of node i generator injection, PDGi It is the active power of node i access distributed generation resource injection, ViFor the node voltage of node i, VjFor the node voltage of node j, GijThe feeder line conductance between node i and j, BijThe feeder line susceptance between node i and j, wherein PLDi,tFor the negative of node i t Lotus active power, θijPhase angle difference between node i and j, QLDi,tFor the reactive load power of node i t, ai,tFor node The load growth coefficient of i t, PLDi,t-1For the load active power of node i t-1, QLDi,t-1For node i t-1's Reactive load power.
It should be noted that the step 203 in the embodiment of the present application is consistent with the step 102 in a upper embodiment, herein No longer it is described in detail.
Step 204, dragonfly algorithm parameter and initialization of population.
Step 205, the initial fitness function value for calculating dragonfly individual in population.
Step 206 optimizes object of planning function according to 5 kinds of behaviors of dragonfly algorithm, constantly iterates to calculate, and updates The position of dragonfly individual.
Step 207, output iteration reach corresponding maximum adaptation degree functional value and dragonfly when default maximum number of iterations Body obtains distributed generation resource optimum programming scheme.
It is asked it should be noted that carrying out multiple-objection optimization to object of planning function using dragonfly algorithm in the embodiment of the present application Solution, using all node photovoltaic power generation capacities of distributed generation resource and wind-power electricity generation capacity as the dragonfly population of algorithm, setting is calculated The number of iterations and the condition of convergence of method calculate the fitness function of dragonfly individual, carry out optimizing to dragonfly individual, update dragonfly The position of body, it is optimum programming scheme that output, which obtains the convergence result that optimization calculates,.The algorithm flow schematic diagram of dragonfly algorithm As shown in figure 4, its detailed process are as follows:
Load data and meteorological data are obtained by random sampling, and meteorological data includes photometric data and air speed data, right Algorithm parameter is initialized, and the parameter of initialization includes weight, the number of iterations, the condition of convergence and population, and initialization population can It indicates are as follows:
X=[PPV1N,...,PPVnN,PWG1N,...,PWGnN]；
Wherein, PPVi、PWGiIt is the capacity of distributed photovoltaic power generation and distributed wind-power generator that i-th of node accesses, i.e., A series of initial programmes.
Calculate the initial fitness function value of dragonfly individual, calculation are as follows:
K=K0-WF；
Wherein, K is fitness function value, K0For preassigned one big tree, W is penalty factor, and F is total cost mesh Mark.
A dragonfly location updating of every progress, just calculates the maximum adaptation angle value of primary current dragonfly, and saves maximum Fitness value and its corresponding dragonfly individual.
Dragonfly algorithm considers that 5 kinds of behaviors of dragonfly population optimize calculating to objective function, comprising:
Dispersion behavior:
Wherein, SiIndicate the dispersion behavior of i-th of individual, X indicates the position of current individual, XjIndicate j-th of adjacent body Position, n be adjacent body sum.In the embodiment of the present application, XjFor j-th of programme.
Synchronization line are as follows:
Wherein, AiIndicate the synchronization behavior of i-th of individual, VjIndicate the speed of j-th of adjacent body, n is adjacent body Sum, N are individual sum.
Cohesion behavior:
Wherein, CiIndicate the cohesion behavior of i-th of individual, X is the position of current individual, XjIndicate j-th of adjacent body Position, n are the sum of adjacent body.
Predation:
Dragonfly is intended to the positioning of food during predation are as follows:
Wherein, FiIndicate that the predation of i-th of individual, X are the position of current individual,Indicate the position of food source.
Keep away enemy's behavior:
Dragonfly evades natural enemy is defined as:
Wherein, EiIndicate i-th of individual keeps away enemy's behavior, and X is the position of current individual,Indicate the position of current natural enemy It sets.
Each dragonfly behavior degree (i.e. S can be calculated according to above-mentioned formulai、Ai、Ci、FiAnd Ei)。
It is constantly iterated calculating, updates the position of dragonfly individual, update method are as follows:
ΔXi+1=(sSi+aAi+cCi+fFi+eEi)+wΔXi；
Wherein, s indicates the weight of dispersion behavior；A indicates the weight of synchronous behavior；The weight of c expression cohesion behavior；F table Show characteristic food item coefficient；E indicates natural enemy characteristic coefficient；W is iteration weight；T is iteration count.
The then update of position vector are as follows:
Xt+1=Xt+ΔXt+1。
Judge whether process meets the condition of convergence (termination condition) of algorithm, if algorithm has reached preset greatest iteration Number then exports the individual of dragonfly corresponding to the maximum adaptation angle value and optimal value of dragonfly, obtains the convergence result that optimization calculates As optimum programming scheme, otherwise the number of iterations adds 1, and jump execute consider dragonfly population 5 kinds of behaviors to objective function into Row optimization calculates.
Distributed generation resource multi-objection optimization planning method provided by the embodiments of the present application has the advantage that
(1) fluctuation for considering distributed generation resource active power output, obtained programme more meet reality；
(2) uncertainty for considering distributed generation resource load growth carries out load bus according to cluster analysis result Classification, can accurate simulation actual load growth pattern so that programme more has feasibility, accurately and reliably.
(3) objective function is solved using multiple target dragonfly algorithm, there is the spy for calculating that the time is short, reaction speed is fast Point.
In order to make it easy to understand, referring to Fig. 3, the embodiment of the present application provides a kind of distributed generation resource multi-objection optimization planning One embodiment of device, comprising:
Modeling module 301, for constructing distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution mould Type.
Planning module 302 establishes the constraint item of object of planning function for constructing the object of planning function of distributed generation resource Part, constraint condition include: trend constraint condition, node voltage constraint condition and distributed generation resource injecting power constraint condition, tide Constraint condition is flowed according to distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model foundation.
Optimization module 303, it is defeated for being optimized according to object of planning function of the dragonfly algorithm to distributed generation resource The optimal solution for meeting stopping criterion for iteration out obtains distributed generation resource optimum programming scheme.
Wherein, distributed generation resource power output probabilistic model includes the wind-power electricity generation active power output probability and photovoltaic of distributed generation resource Generate electricity active power output probability, and distributed generation resource load probabilistic distribution model includes the active power and reactive power of node load, The active power of node load is the sum of basic load active power and load active power increasing value of node load, and node is negative The reactive power of lotus is the sum of basic load reactive power and reactive load power increment of node load.
Further, further includes:
Preprocessing module 300 is determined for the history intensity of illumination data and historical wind speed data according to distributed generation resource Photovoltaic power generation active power output probability and wind-power electricity generation active power output probability.
Further, optimization module 303 specifically includes:
Initialization submodule 3031 is used for dragonfly algorithm parameter and initialization of population.
Fitness submodule 3032, for calculating the initial fitness function value of dragonfly individual in population.
Submodule 3033 is updated, for optimizing according to 5 kinds of behaviors of dragonfly algorithm to object of planning function, constantly repeatedly In generation, calculates, and updates the position of dragonfly individual.
Output sub-module 3034, for exporting corresponding maximum adaptation degree function when iteration reaches default maximum number of iterations Value and dragonfly individual, obtain distributed generation resource optimum programming scheme.
The embodiment of the present application also provides a kind of one embodiment of distributed generation resource multi-objection optimization planning equipment equipment, A kind of distributed generation resource multi-objection optimization planning equipment includes processor and memory；
Program code is transferred to processor for storing program code by memory；
Processor is used for according to the instruction execution distributed generation resource multi-objection optimization planning method above-mentioned in program code Distributed generation resource multi-objection optimization planning method in embodiment.
In several embodiments provided herein, it should be understood that disclosed device and method can pass through it Its mode is realized.For example, the apparatus embodiments described above are merely exemplary, for example, the division of the unit, only Only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components can be tied Another system is closed or is desirably integrated into, or some features can be ignored or not executed.Another point, it is shown or discussed Mutual coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING or logical of device or unit Letter connection can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme 's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product When, it can store in a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words It embodies, which is stored in a storage medium, including some instructions are used so that a computer Equipment (can be personal computer, server or the network equipment etc.) executes the complete of each embodiment the method for the application Portion or part steps.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, english abbreviation: ROM), random access memory (full name in English: Random Access Memory, english abbreviation: RAM), the various media that can store program code such as magnetic or disk.
The above, above embodiments are only to illustrate the technical solution of the application, rather than its limitations；Although referring to before Embodiment is stated the application is described in detail, those skilled in the art should understand that: it still can be to preceding Technical solution documented by each embodiment is stated to modify or equivalent replacement of some of the technical features；And these It modifies or replaces, the spirit and scope of each embodiment technical solution of the application that it does not separate the essence of the corresponding technical solution.

Claims (10)

1. a kind of distributed generation resource multi-objection optimization planning method, which comprises the following steps:
101, building distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model；
102, the object of planning function for constructing the distributed generation resource, establishes the constraint condition of the object of planning function, described Constraint condition includes: trend constraint condition, node voltage constraint condition and distributed generation resource injecting power constraint condition, the tide Constraint condition is flowed according to distributed generation resource power output probabilistic model and the distributed generation resource load probabilistic distribution model foundation；
103, it is optimized according to object of planning function of the dragonfly algorithm to the distributed generation resource, output meets iteration end The only optimal solution of condition obtains distributed generation resource optimum programming scheme；
Wherein, the distributed generation resource power output probabilistic model includes the wind-power electricity generation active power output probability and photovoltaic of distributed generation resource Generate electricity active power output probability, the distributed generation resource load probabilistic distribution model include node load active power and idle function Rate.
2. distributed generation resource multi-objection optimization planning method according to claim 1, which is characterized in that before step 101, Further include:
100, according to the history intensity of illumination data and historical wind speed data of distributed generation resource, determine that photovoltaic power generation active power output is general Rate and wind-power electricity generation active power output probability.
3. distributed generation resource multi-objection optimization planning method according to claim 1, which is characterized in that step 103 is specific Include:
1031, dragonfly algorithm parameter and initialization of population；
1032, the initial fitness function value of dragonfly individual in population is calculated；
1033, the object of planning function is optimized according to the 5 of dragonfly algorithm kinds of behaviors, is constantly iterated to calculate, update dragonfly The position of dragonfly individual；
1034, output iteration reaches corresponding maximum adaptation degree functional value and dragonfly individual when default maximum number of iterations, obtains Distributed generation resource optimum programming scheme.
4. distributed generation resource multi-objection optimization planning method according to claim 1, which is characterized in that the distributed electrical The construction method of source load probabilistic distribution model are as follows:
1011, the historical load data of the node load of distributed generation resource is obtained；
1012, the historical load data is clustered according to K-Mediods clustering methodology, determines initial cluster center；
1013, the Euclidean distance for calculating the initial cluster center and each historical load data, according to the initial clustering Center and the Euclidean distance complete the clustering of each historical load data, obtain cluster result；
1014, the corresponding historical load data of each cluster result is fitted according to cluster result, is determined all kinds of The classification growth factor of load；
1015, according to the load power of each node and the corresponding classification growth factor, distributed generation resource Load Probability is constructed Distributed model.
5. distributed generation resource multi-objection optimization planning method according to claim 1, which is characterized in that the object of planning Function specifically:
Min F=λ1CDG+λ2CLoss；
Wherein, F is total cost target, λ1、λ2For the weight of sub-goal, CDGFor distributed generation resource investment cost, r is load equipment yearly depreciation, ciIt is installed for node i single The expense of bit capacity distributed generation resource, SDGiThe capacity of distributed generation resource, C are installed for node iLossFor system losses cost, CpsFor For unit sales rate of electricity, PlLossFor the active loss of the l articles branch, TmaxHourage, N is lost for annual peak loadlIt is total for system Circuitry number.
6. distributed generation resource multi-objection optimization planning method according to claim 5, which is characterized in that the trend constraint Condition specifically:
Wherein, PGiFor the active power of node i generator injection, QGiFor the reactive power of node i generator injection, PDGiIt is section Point i accesses the active power of distributed generation resource injection, ViFor the node voltage of node i, VjFor the node voltage of node j, GijFor Feeder line conductance between node i and j, BijThe feeder line susceptance between node i and j, PLDi,tFor the load wattful power of node i t Rate, θijPhase angle difference between node i and j, QLDi,tFor the reactive load power of node i t, ai,tFor node i t's Load growth coefficient, PLDi,t-1For the load active power of node i t-1, QLDi,t-1For the reactive load of node i t-1 Power.
7. a kind of distributed generation resource multi-objection optimization planning device characterized by comprising
Modeling module, for constructing distributed generation resource power output probabilistic model and distributed generation resource load probabilistic distribution model；
Planning module establishes the constraint of the object of planning function for constructing the object of planning function of the distributed generation resource Condition, the constraint condition include: trend constraint condition, node voltage constraint condition and distributed generation resource injecting power constraint item Part, the trend constraint condition is according to distributed generation resource power output probabilistic model and the distributed generation resource load probabilistic distribution Model foundation；
Optimization module is exported for being optimized according to object of planning function of the dragonfly algorithm to the distributed generation resource The optimal solution for meeting stopping criterion for iteration obtains distributed generation resource optimum programming scheme；
Wherein, the distributed generation resource power output probabilistic model includes the wind-power electricity generation active power output probability and photovoltaic of distributed generation resource Generate electricity active power output probability, the distributed generation resource load probabilistic distribution model include node load active power and idle function Rate.
8. distributed generation resource multi-objection optimization planning device according to claim 7, which is characterized in that further include:
Preprocessing module determines that photovoltaic is sent out for the history intensity of illumination data and historical wind speed data according to distributed generation resource Electric active power output probability and wind-power electricity generation active power output probability.
9. distributed generation resource multi-objection optimization planning device according to claim 7, which is characterized in that the optimization module It specifically includes:
Initialization submodule is used for dragonfly algorithm parameter and initialization of population；
Fitness submodule, for calculating the initial fitness function value of dragonfly individual in population；
Update submodule, for optimizing according to 5 kinds of behaviors of dragonfly algorithm to the object of planning function, continuous iteration meter It calculates, updates the position of dragonfly individual；
Output sub-module, for exporting corresponding maximum adaptation degree functional value and dragonfly when iteration reaches default maximum number of iterations Individual obtains distributed generation resource optimum programming scheme.
10. a kind of distributed generation resource multi-objection optimization planning equipment, which is characterized in that the equipment includes processor and storage Device；
Said program code is transferred to the processor for storing program code by the memory；
The processor is used for according to the instruction execution distributed electrical described in any one of claims 1-6 in said program code Source multi-objection optimization planning method.
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