CN109934450A - More scene active distribution network planning appraisal methods based on Demand Side Response - Google Patents
More scene active distribution network planning appraisal methods based on Demand Side Response Download PDFInfo
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Abstract
The active distribution network planning appraisal method based on Demand Side Response that the present invention relates to a kind of, its technical characterstic is: the following steps are included: step 1, comprehensively considering photovoltaic power generation output power, the uncertainty that loading demand and future load increase, carrying out the uncertainties model that photovoltaic power generation output power, loading demand and future load increase；Step 2 establishes Model for Multi-Objective Optimization and its network constraint condition using probability density function；Step 3 analyzes generating probability density function by Monte Carlo simulation, in conjunction with the multiple objective function of weighted factor method solution procedure 2.The present invention can active distribution network runs control strategy and the improvement of photovoltaic power generation ability and the reduction of operating cost and power loss may be implemented in Demand Side Response by correctly implementing.
Description
Technical field
The invention belongs to technical field of electric power, are related to active distribution network planning appraisal method, especially a kind of to be based on demand
More scene active distribution network planning appraisal methods of side response.
Background technique
Currently, distributed generation resource and renewable energy are constantly applied to the design, exploitation and operation of distribution network, promote
Distribution network develops towards active distribution network.Active distribution network is defined as that electricity can be conveyed to user with intelligence and controlled way
The power grid of power.In fact, the advantage of active distribution network is that it can be improved the reliability of user side and responding ability, and help
Distribution network operator makes better decision.Therefore, Demand Side Response is an important component of active distribution network.Knot
The photovoltaic power generation quantity of injection power distribution network is closed, while considering that coordinating voltage control and the control of adaptive power factor etc. actively matches
Power grid control strategy urgently needs to provide at present the side of the route operating cost and power loss under a kind of response of evaluation requirement side
Method, thus under different scenes distribution network operation grade and applicability make objective and comprehensive analytical judgment.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, propose a kind of more scenes active based on Demand Side Response
Distribution network planning appraisal procedure, can under different scenes distribution network operation grade and applicability make it is objective and comprehensive
Analytical judgment.
The present invention solves its realistic problem and adopts the following technical solutions to achieve:
A kind of active distribution network planning appraisal method based on Demand Side Response, comprising the following steps:
Step 1 comprehensively considers the uncertainty that photovoltaic power generation output power, loading demand and future load increase, and carries out
The uncertainties model that photovoltaic power generation output power, loading demand and future load increase；
Step 2 establishes Model for Multi-Objective Optimization and its network constraint condition using probability density function；
Step 3 analyzes generating probability density function by Monte Carlo simulation, in conjunction with weighted factor method solution procedure 2
Multiple objective function.
Moreover, the specific steps of the step 1 include:
(1) uncertainties model of photovoltaic power generation output power: the power that photovoltaic power generation generates depends on three parameters, i.e.,
The characteristic of solar irradiance, site environment temperature and module itself；According to beta probability density function, solar irradiance is described such as
Under:
In above formula, behalf solar irradiance, unit kW/m2；
In order to calculate the α in Beta probability density function, β value is obtained by the average value mu and standard deviation sigma of stochastic variable
Out:
Photovoltaic generation power transfer function based on irradiation level is expressed as follows:
Ppv(s)=ηpv×Spv×s
Wherein, Ppv(s) the photovoltaic power generation output power when irradiation intensity is s, unit kW, η are representedpvAnd SpvRespectively
It is the efficiency (%) and the gross area (m of photovoltaic generating system2)；
(2) uncertainty of loading demand: the load on feeder line is modeled as follows by ordinary power density function:
(3) uncertainty that future load increases: assuming that the initial load on feeder line i isFeeder line in project period t
On load growth beThen the power density functions of load growth can be according to considered below:
The growth of load in t on feeder line i can indicate are as follows:
Moreover, the specific steps of the step 2 include:
(1) objective function is determined:
fobj=w1fobj1+w2fobj2
Wherein, fobjIt is total objective function；w1And w2It is weight factor, and meets w1+w2=1.fobj1Be route operation at
This, NT represents planning horizon；Wherein fobj1Represent the operating cost of photovoltaic power generation, fobj2Represent operating cost when demand is reduced；Indicate electricity price when photovoltaic power generation equipment increases in feeder line i and t or reduce active power；Indicate photovoltaic power generation
The active power that equipment generates in power generation feeder line i and t；It is the electricity that the loading demand l in feeder line i and t is provided
Valence is dispatched to the active power in side response management of demaning reduction,It is that loading demand l exists in Demand Side Response management
Active power reduction amount in feeder line i and t；fobj2It represents from the route that planning angle considers and runs power loss；Vi,t, δi,t
And Vj,t, δj,tIt is feeder line i, voltage magnitude and voltage phase angle of the j in t respectively；
(2) network constraint condition is determined:
1. equality constraint: annual active power and reactive power equilibrium in each feed line
Wherein, wherein GijAnd BijBe respectively in feeder line admittance matrix corresponding with ith row and jth column the real part of element and
Imaginary part, TijIt is the tap magnetic flux of on-load tap changers of transformers, NbusIt is feeder line number；
2. inequality constraints condition:
Branch's constraint condition:
Wherein,It is the maximum current value of route；
Voltage value range in each feed line:
Vi min≤Vi,t≤Vi max
Wherein, Vi,tAnd δi,tIt is the voltage magnitude and voltage phase angle in feeder line i and t, V respectivelyi maxAnd Vi minAndWithCorresponding voltage amplitude and voltage phase angle maximin；
Photovoltaic power generation constraint condition:
Wherein, Pg,tAnd Qg,tRespectively correspond the active power and reactive power generated in annual each feed line；With
AndWithRespectively correspond photovoltaic power generation active power and reactive power maximin.SmaxIt is photovoltaic inversion device
Maximum capacity；
Capacity constraints at balance nodes:
Wherein, Pb,tAnd Qb,tRespectively indicate the active power and reactive power at t balance nodes；WithAndWithRespectively represent the active power and reactive power maximin at balance nodes；
On-load tap changers of transformers adjusts constraint condition:
Wherein, TijThe regulated value of on-load tap changers of transformers is represented,WithRespectively correspond adjusting maximum and most
Small value；
The power-factor angle of photovoltaic generating system:
Wherein, φg,tIndicate the photovoltaic generating system power-factor angle of t,WithRespectively correspond power-factor angle
Maximum and minimum value；
Demand Side Response constraint condition:
Wherein,WithIt is maximum wattful power of the loading demand l in feeder line i and t in Demand Side Response management
Rate and reactive power reduced value；
It can obtain from the above analysis, the optimized variable of multi-objective optimization question includes:
The advantages of the present invention:
1, more scene active distribution network planning appraisal method and devices based on Demand Side Response that the invention proposes a kind of,
Angle is run and planned from power distribution network, considers that unpredictable workloads and network constraint condition, this method reduce to the maximum extent
The operating cost and power loss of route.Analyze solar irradiance, the randomness that loading demand and future load increase, using general
Rate density function is modeled.Generating probability density function is analyzed by Monte Carlo simulation, is solved in conjunction with weighted factor method more
Objective optimisation problems.Compared with conventional electrical distribution net, control strategy and Demand Side Response are run by correctly implementing active distribution network
The improvement of photovoltaic power generation ability and the reduction of operating cost and power loss may be implemented.
2, the present invention is in order to select most suitable active distribution network operation control strategy, each scheme or their combination
Consider economy.And in order to assess the economic feasibility of each scheme, it is considered comprehensively including reducing power distribution network energy loss, light
Lie prostrate production capacity and the key factors such as possessed strong property of rack compared with conventional electrical distribution net.
3, it is negative to consider photovoltaic power generation output power, loading demand and future to uncertainties model process synthesis of the invention
Lotus increases the influence that three kinds of uncertain factors run power distribution network and plan, can consider the master based on distributed generation resource comprehensively
Dynamic power distribution network operation characteristic, improves the accuracy and accuracy of modeling
4, the present invention is based on the foundation of the objective function of distribution network line operating cost and power loss on the one hand to consider
Such as variation of voltage dip, load curve and other influences to power distribution network operational process in power distribution network, while being capable of basis
The permeability of renewable distributed generation resource assesses available power distribution network capacity, from the investment and Demand Side Response two to power distribution network
A level analysis active distribution network facilitates the realization of efficient system operation.
5, the present invention can guarantee that sample is considering the active distribution in Demand Side Response using Monte Carlo simulation analysis
Fast convergence finds Function Optimization solution under the conditions of net operation reserve and network constraint, guarantees route operating cost and power loss
It minimizes.
Detailed description of the invention
Fig. 1 is Monte Carlo simulation flow chart of the invention；
Fig. 2 is of the invention based on the modified 16 feeder lines distribution network wiring diagram of IEEE14；
Fig. 3 (a) is 24 hours solar irradiance curve graphs of the invention；
Fig. 3 (b) is solar irradiance histogram of the invention；
Fig. 4 (a) is loading demand histogram of the invention；
Fig. 4 (b) is rate of load growth histogram of the invention.
Specific embodiment
The embodiment of the present invention is described in further detail below in conjunction with attached drawing:
The active distribution network planning appraisal method based on Demand Side Response that the invention proposes a kind of, this method consider first
The uncertainty that solar irradiance, workload demand and future load increase, with probability density function founding mathematical models.And root
According to the different scenes such as voltage control and the control of adaptive power factor are coordinated, cooperate programmed decision-making, it is then imitative using Monte Carlo
True method solves the multi-objective optimization question in planned range using the probability density function and weighted factor of generation.
A kind of active distribution network planning appraisal method based on Demand Side Response, comprising the following steps:
Step 1 comprehensively considers the uncertainty that photovoltaic power generation output power, loading demand and future load increase, and carries out
The uncertainties model that photovoltaic power generation output power, loading demand and future load increase；
The specific steps of the step 1 include:
(1) uncertainties model of photovoltaic power generation output power: the power that photovoltaic power generation generates depends on three parameters, i.e.,
The characteristic of solar irradiance, site environment temperature and module itself；According to beta probability density function, solar irradiance is described such as
Under:
Wherein, behalf solar irradiance, unit kW/m2.In order to calculate the α in Beta probability density function, β value, by
The average value mu and standard deviation sigma of stochastic variable, obtain:
Photovoltaic generation power transfer function based on irradiation level is expressed as follows:
Ppv(s)=ηpv×Spv×s(3)
Wherein, Ppv(s) the photovoltaic power generation output power when irradiation intensity is s, unit kW, η are representedpvAnd SpvRespectively
It is the efficiency (%) and the gross area (m of photovoltaic generating system2)；Letter is converted to power according to given irradiance distribution and irradiation level
Several influences, the output power distribution of available photovoltaic power generation；
(2) uncertainty of loading demand: the load on feeder line is modeled as follows by ordinary power density function:
(3) uncertainty that future load increases: assuming that the initial load on feeder line i isFeeder line in project period t
On load growth beThen the power density functions of load growth can be according to considered below:
The growth of load in t on feeder line i can indicate are as follows:
Step 2 establishes Model for Multi-Objective Optimization and its network constraint condition using probability density function；
The specific steps of the step 2 include:
(1) determine objective function: minimum power losses have positive effect to distribution network, such as reduce pressure drop, electricity
Line of buckling improvement and other economy and environmental advantage.From the perspective of power distribution network operation, it is necessary to according to renewable distribution
The permeability of power generation assesses available power distribution network capacity, without carrying out additional investment to power distribution network.It is rung by Demand-side
It answers, can realize more efficient system operation using role of each load side in active distribution network management and effect.Cause
This, the project study target proposed is in the case where combining active distribution network control strategy, from the angle of power distribution network operation
Consider the power loss of Demand Side Response scheme and route, so that route operating cost be minimized, objective function is established as follows:
fobj=w1fobj1+w2fobj2
Wherein, fobjIt is total objective function；w1And w2It is weight factor, and meets w1+w2=1.fobj1Be route operation at
This, NT represents planning horizon；Wherein first part fobjRepresent the operating cost of photovoltaic power generation, second part fob2The demand of representative subtracts
Operating cost when few；Indicate electricity when photovoltaic power generation equipment increases in feeder line i and t or reduce active power
Valence；Indicate the active power that photovoltaic power generation equipment generates in power generation feeder line i and t；It is in feeder line i and t
The electricity price that loading demand l is provided is dispatched to the active power in side response management of demaning reduction,It is Demand Side Response
Active power reduction amount of the loading demand l in feeder line i and t in management；fobj2Represent the route fortune considered from planning angle
Row power loss；Vi,t, δi,tAnd Vj,t, δj,tIt is feeder line i, voltage magnitude and voltage phase angle of the j in t respectively；
(2) network constraint condition is determined:
1. equality constraint: annual active power and reactive power equilibrium in each feed line
Wherein, wherein GijAnd BijBe respectively in feeder line admittance matrix corresponding with ith row and jth column the real part of element and
Imaginary part, TijIt is the tap magnetic flux of on-load tap changers of transformers, NbusIt is feeder line number.
2. inequality constraints condition:
Branch's constraint condition:
Wherein,It is the maximum current value of route.
Voltage value range in each feed line:
Wherein, Vi,tAnd δi,tIt is the voltage magnitude and voltage phase angle in feeder line i and t, V respectivelyi maxAnd Vi minAndWithCorresponding voltage amplitude and voltage phase angle maximin.
Photovoltaic power generation constraint condition:
Wherein, Pg,tAnd Qg,tRespectively correspond the active power and reactive power generated in annual each feed line；With
AndWithRespectively correspond photovoltaic power generation active power and reactive power maximin.SmaxIt is photovoltaic inversion device
Maximum capacity.
Capacity constraints at balance nodes:
Wherein, Pb,tAnd Qb,tRespectively indicate the active power and reactive power at t balance nodes；WithAndWithRespectively represent the active power and reactive power maximin at balance nodes.
On-load tap changers of transformers adjusts constraint condition:
Wherein, TijThe regulated value of on-load tap changers of transformers is represented,WithRespectively correspond adjusting maximum and most
Small value.
The power-factor angle of photovoltaic generating system:
Wherein, φg,tIndicate the photovoltaic generating system power-factor angle of t,WithRespectively correspond power-factor angle
Maximum and minimum value.
Demand Side Response constraint condition:
Wherein,WithIt is maximum wattful power of the loading demand l in feeder line i and t in Demand Side Response management
Rate and reactive power reduced value.
It can obtain from the above analysis, the optimized variable of multi-objective optimization question includes:
Step 3 analyzes generating probability density function by Monte Carlo simulation, in conjunction with weighted factor method solution procedure 2
Multiple objective function；
Consider the probability density function that random variation, workload demand and the future load of solar irradiance increase.In conjunction with master
Dynamic power distribution network operation control strategy and heterogeneous networks constraint condition, carry out multiple-objection optimization, route operating cost and power are damaged
Consumption minimizes.Its process is as shown in Figure 1:
Based on 14 feeder line distribution network of IEEE, control strategy is run to embody active distribution network, realizes power supply area
Between coordinated control, in former feeder line distribution network increase by two transformer on-load voltage regulating incoming feeders, i.e. 16 feeder lines, amendment
Distribution network wiring diagram afterwards is as shown in Figure 2.Incoming feeder is by two identical 30MVA transformer-supplieds.Positioned at feeder line 1 and 2
Between two on-load regulator transformer secondary side voltage per unit values be 1.05p.u..Voltage regulator be located at feeder line 8 and 9 it
Between, voltage per unit value is 1.03p.u.Voltage value range is ± the 6% of per unit value, i.e. Vi min=0.94p.u. and Vi max=
1.06p.u..The power factor φ of photovoltaic generating systemg,tFrom advanced 0.95 to lag 0.95.Assuming that feeder line 5,11 and 16 is three
A possible photovoltaic power generation position, these three photovoltaic power generation positions respectively represent a load center (at feeder line 5), city head of district feedback
The long feeder line of line (at feeder line 11) and rural area (at feeder line 16), this combination provides different voltage raising and lowering feelings
Condition.
3 15MW photo-voltaic power generation stations are installed on 5,11 and No. 16 feeder lines.Each power station is by 15 1MW solar-electricities
Pond board group is at photoelectric conversion rate ηpv=18.6%, effective area Spv=10m2.In the Beta probability density function of solar irradiance
Parameter alpha=6.5, β=3.5.
In conjunction with the Beta probability density function of the average daily solar irradiance per hour and consideration solar radiation of Fig. 3 (a) and (b)
Histogram and Fig. 4 (a) and the loading demand of (b) and the probability density function histogram of the growth of load.It will be above-mentioned special based on covering
The probability density function of Carlow simulation analysis be applied to Fig. 2 distribution network wiring diagram, and by GAMS plan strategies for optimization software reality
It is existing, while being solved using multiple constraint nonlinear optimization IPOPT solver.Random Load demand and solar irradiance Hour
It calculates, it is corresponding with 26,280 (3x8760) a samples based on Monte Carlo simulation analysis in 3 year project period.In sample program
In, possible sample value is calculated by the hour in t.Wherein, each year is equal to 8760 sampling hours in planned range.
From the angle of sensitivity analysis, formula f is consideredobj=w1fobj1+w2fobj2It is middle difference weight factor it is appropriately combined.
Weighted factor changes to 0.9 from 0.1, step-length 0.05, and meets w1+w2=1.Then, by solving each combined target letter
Number selects the weighted array with minimum value.
Influence in order to illustrate active distribution network management and Demand Side Response to route operating cost and power loss, table 1
List the various combination of the active distribution network management and Demand Side Response under four kinds of scenes.
Four kind scenes of the table 1 based on active distribution network management and Demand Side Response
Table 2 lists the target function value under different scenes.
Objective function under the different weight combinations of table 2
In scenario A, route operating cost and power loss respectively may be about 451 yuan/h and 686kWh.In scenario B, with field
Scape A is compared, these values have dropped 11% and 10% respectively.In scene C, it is contemplated that active distribution network runs control strategy, line
Road operating cost and power loss respectively may be about 353 yuan/h and 583kWh, and wherein target function value is reduced about compared with scenario A
21% and 15%.In scene D, it is contemplated that active distribution network runs control strategy and Demand Side Response, compared with other scenes,
The value of objective function is minimum.Compared with scenario A, the reduction amount of route operating cost and power loss respectively may be about 27% He
25%.Therefore, running control strategy and Demand Side Response by using active distribution network can produce compared with conventional electrical distribution net
Raw more photovoltaic energies, while reducing route operating cost and power loss.
It is emphasized that embodiment of the present invention be it is illustrative, without being restrictive, therefore the present invention includes
It is not limited to embodiment described in specific embodiment, it is all to be obtained according to the technique and scheme of the present invention by those skilled in the art
Other embodiments, also belong to the scope of protection of the invention.
Claims (3)
1. a kind of active distribution network planning appraisal method based on Demand Side Response, it is characterised in that: the following steps are included:
Step 1 comprehensively considers the uncertainty that photovoltaic power generation output power, loading demand and future load increase, and carries out photovoltaic
The uncertainties model that output power, loading demand and the future load of generating electricity increase；
Step 2 establishes Model for Multi-Objective Optimization and its network constraint condition using probability density function；
Step 3 analyzes generating probability density function by Monte Carlo simulation, in conjunction with more mesh of weighted factor method solution procedure 2
Scalar functions.
2. a kind of active distribution network planning appraisal method based on Demand Side Response according to claim 1, feature exist
In: the specific steps of the step 1 include:
(1) uncertainties model of photovoltaic power generation output power: the power that photovoltaic power generation generates depends on three parameters, the i.e. sun
The characteristic of irradiation level, site environment temperature and module itself；According to beta probability density function, solar irradiance is described as follows:
In above formula, behalf solar irradiance, unit kW/m2；
In order to calculate the α in Beta probability density function, β value is obtained by the average value mu and standard deviation sigma of stochastic variable:
Photovoltaic generation power transfer function based on irradiation level is expressed as follows:
Ppv(s)=ηpv×Spv×s
Wherein, Ppv(s) the photovoltaic power generation output power when irradiation intensity is s, unit kW, η are representedpvAnd SpvIt is light respectively
The efficiency (%) and the gross area (m of photovoltaic generating system2)；
(2) uncertainty of loading demand: the load on feeder line is modeled by ordinary power density function
It is as follows:
(3) uncertainty that future load increases: assuming that the initial load on feeder line i isIn project period t on feeder line
Load growth isThen the power density functions of load growth can be according to considered below:
The growth of load in t on feeder line i can indicate are as follows:
3. a kind of active distribution network planning appraisal method based on Demand Side Response according to claim 1, feature exist
In: the specific steps of the step 2 include:
(1) objective function is determined:
fobj=w1fobj1+w2fobj2
Wherein, fobjIt is total objective function；w1And w2It is weight factor, and meets w1+w2=1；fobj1It is route operating cost,
NT represents planning horizon；Wherein, fobj1Represent the operating cost of photovoltaic power generation, fobj2Represent operating cost when demand is reduced；Indicate electricity price when photovoltaic power generation equipment increases in feeder line i and t or reduce active power；Indicate photovoltaic power generation
The active power that equipment generates in power generation feeder line i and t；It is the electricity that the loading demand l in feeder line i and t is provided
Valence is dispatched to the active power in side response management of demaning reduction,It is that loading demand l exists in Demand Side Response management
Active power reduction amount in feeder line i and t；fobj2It represents from the route that planning angle considers and runs power loss；Vi,t, δi,t
And Vj,t, δj,tIt is feeder line i, voltage magnitude and voltage phase angle of the j in t respectively；
(2) network constraint condition is determined:
1. equality constraint: annual active power and reactive power equilibrium in each feed line
Wherein, wherein GijAnd BijIt is the real and imaginary parts of element in feeder line admittance matrix corresponding with ith row and jth column respectively,
TijIt is the tap magnetic flux of on-load tap changers of transformers, NbusIt is feeder line number；
2. inequality constraints condition:
Branch's constraint condition:
Wherein,It is the maximum current value of route；
Voltage value range in each feed line:
Vi min≤Vi,t≤Vi max
Wherein, Vi,tAnd δi,tIt is the voltage magnitude and voltage phase angle in feeder line i and t, V respectivelyi maxAnd Vi minAnd δi maxWithCorresponding voltage amplitude and voltage phase angle maximin；
Photovoltaic power generation constraint condition:
Wherein, Pg,tAnd Qg,tRespectively correspond the active power and reactive power generated in annual each feed line；WithAndWithRespectively correspond photovoltaic power generation active power and reactive power maximin；SmaxBe photovoltaic inversion device most
Large capacity；
Capacity constraints at balance nodes:
Wherein, Pb,tAnd Qb,tRespectively indicate the active power and reactive power at t balance nodes；WithAndWithRespectively represent the active power and reactive power maximin at balance nodes；
On-load tap changers of transformers adjusts constraint condition:
Wherein, TijThe regulated value of on-load tap changers of transformers is represented,WithRespectively correspond the minimum and maximum of adjusting
Value；
The power-factor angle of photovoltaic generating system:
Wherein, φg,tIndicate the photovoltaic generating system power-factor angle of t,WithRespectively correspond the maximum of power-factor angle
And minimum value；
Demand Side Response constraint condition:
Wherein,WithBe Demand Side Response management in maximum active power of the loading demand l in feeder line i and t with
Reactive power reduced value；
It can obtain from the above analysis, the optimized variable of multi-objective optimization question includes:
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