CN105552941B - A kind of distributed generation resource peak capacity optimization method - Google Patents
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Abstract
The present invention proposes a kind of distributed generation resource peak capacity optimization method, and a year based model for load duration curve is drawn out according to yearly load curve, and a peak value adjusts and carries out distribution network planning under load level at maximum load.Therefore present invention reduces investment amount, and utilization rate of equipment and installations is improved.
Description
Technical field
The present invention proposes a kind of distributed generation resource peak capacity optimization method, belongs to power distribution network Peak Shaving.
Background technology
Counted according to The National Electric Power Communication Center, China each department peak load of grid increase reach 10 with
On, low ebb load amplification is but no more than 5 percent, or even there are low ebb load negative growth phenomenon.According to the minimum day in somewhere
Rate of load condensate carries out the analysis of load peak valley and understands, summer, average minimum daily load rate was 0.561, and in winter 0.59, peak-valley difference reaches
25GW.In other words, load peak-valley difference is increasing, and peak regulation arduous task, there is an urgent need to peak value is considered in Electric Power Network Planning
The control of load, this is conducive to the benign development of power grid.
Traditional distribution network planning problem refers on the premise of meeting to constrain customer power supply and the network operation,
Seek one group of optimal decision variable (power transformation station location and capacity, the path of feeder line and size etc.), make investment, operation, maintenance,
The sum of network loss and reliability failure costs are minimum.Conventional electrical distribution network planning stroke is generally based on peak load and is planned, this control gauge
It is big to draw investment, very flexible, and the utilization rate of equipment and installations after planning is low.
Since distributed generation resource has the advantages that economic, flexible, environmental protection and delays distribution network construction, obtain more and more
Using.Some, based on the horizontal lower branch overload situations of different peak loads, is used according to year based model for load duration curve in the prior art
A kind of heuritic approach carries out the addressing of distributed generation resource, wherein considering the peak regulation effect of oil-burning machine and gas engine respectively.Have
When considering electric automobile access network, the electric automobile charging station of the function containing V2G can utilize the electric automobile that leaves unused as
Electric energy is fed back to power grid by energy storage device in peak times of power consumption, realizes that electric automobile participates in matching somebody with somebody as portable distribution energy-storage units
Peak load regulation network, realizes the optimization of power distribution network.
The changing rule of load curve is the basis of distribution network planning.The load character of the newly-established high speed collar region in certain city
It is that office office building is more, secondly residential area, without factory, with reference to specific data from curve tendency, peak-valley difference
Rate and duration of peaking time analyze part throttle characteristics.Shown in formula specific as follows：
Δ T=T90% (2)
In formula：%P is peak-valley ratio, PmaxFor peak load, PminFor minimum load, Δ T is duration of peaking time,
T90%It it was 90% peak load duration, it is specified that being duration of peaking time.
The characteristics of to find out the peak feature of different time and peak capacity, different times bears typical day in selecting 1 year
Lotus is analyzed, as shown in Figure 1.
As seen from Figure 1, in addition to except July, 20 daily load curve has single-peak response, all there is morning peak in four days in remaining
With evening peak；The morning peak situation higher than evening peak is more in bimodal load；But as temperature reduces, in evening peak on December 20
Load is higher than morning peak；The different corresponding peak-valley ratios of load curve peak feature differ greatly, and have the July of single-peak response
Peak-valley ratio on the 20th has reached 78%, and evening peak is 62% higher than the peak-valley ratio on December 20 of morning peak, and morning peak ratio is late
Three days peak-valley ratios of high peak height are respectively 52.5%, 64% and 74%, and therefore, it is necessary to different peak capacities.
The content of the invention
Goal of the invention：The present invention proposes a kind of distributed generation resource peak capacity optimization method, utilizes the distribution in power distribution network
Formula power supply, which is contributed, carries out peak regulation, improves flexibility and the capacity utilization of peak regulation.
Technical solution：The present invention proposes a kind of distributed generation resource peak capacity optimization method, comprises the following steps：
1) distributed area and probability of peak-valley ratio are counted according to historical data, and establish power generation, transmission of electricity cost with
Relation between capacity；
2) variance of each Time comparison load is normalized with duration of peaking time, draws object function
Expression formula；
3) the load curve conduct of that day where peak load point in the section is selected in each peak-valley ratio section
Initial load is horizontal；
4) chromosome coding is carried out, produces initial population；
5) calculating of fitness is calculated, and carries out the comparison of fitness, retains larger fitness value；
6) judge whether to meet end condition, the jump procedure 7 if meeting)；Otherwise start to select, intersect, make a variation and jump
Go to step 5)；
7) draw optimum population, be compared in colony, that output variance is minimum respectively and that economic benefit is best two
A scheme.
Preferably, the object function obtained in the step 2) after normalized is：
f2=min (Cc, Cj)
In formula, CcFor distributed generation resource cost, CjFor the energy-saving benefit of distributed generation resource access, f2After normalization
Object function, (kf+ks) it is unit capacity cost of electricity-generating, Transmission Cost cost coefficient, Δ P is optimizing capacity, and f (Δ P, T) is
Interruption cost, it is related with optimizing capacity and duration,For distributed generation resource investment cost；giFor emission reduction
Amount of carbon dioxide, its unit are ton；ai, bi, ciFor function coefficients, rule of thumb draw；kjjFor unit carbon dioxide emission reduction amount institute
The economic profit coefficients brought, PiFor the comparison load at each moment, k is coefficient, PDGjFor distributed generation resource capacity.
Preferably, the step 2) further includes following constraints：
Lmin≤L≤Lmax
L is load level in formula, LminIt is a year minimum load, LmaxIt is annual peak load；
PG≤Pmax
P in formulaGIt is distributed generation resource spare capacity, PmaxIt is Peak Load；
%Pimin≤ %Pi≤ %Pimax
%P in formulaiFor peak-valley ratio, %PimaxIt is maximum peak-valley ratio, %PiminIt is minimum peak-valley ratio；
ΔTimin≤ΔTi≤ΔTimax
Δ T in formulaiFor duration of peaking time, Δ TimaxFor peak-peak load duration, Δ TiminFor smallest peaks
The duty value duration.
Preferably, chromosome coding uses real coding, in units of 0.1MW, chromosome length etc. in the step 4)
Number of regions is divided in power distribution network.
Preferably, the end condition in the step 6) is that iterations reaches maximum iteration.In the step 6)
The selected as optimum maintaining strategy.Intersect described in the step 6) and intersect for single-point.
Beneficial effect：The present invention draws out a year based model for load duration curve according to yearly load curve, at maximum load a peak
Value adjusts and carries out distribution network planning under load level.Therefore present invention reduces investment amount, and utilization rate of equipment and installations is improved.
Brief description of the drawings
Fig. 1 encloses at a high speed typical day load curve figure for certain；
Fig. 2 is photovoltaic power curve figure；
Fig. 3 is wind-driven generator power curve figure；
Fig. 4 is the equivalent load curve map after access solar energy power generating；
Fig. 5 is the equivalent load curve map after access wind-driven generator；
Fig. 6 is access different capabilities solar photovoltaic power equivalent load figure；
Tu7Wei Mou cities high speed collar region division figure；
Fig. 8 is operational flowchart of the present invention；
Fig. 9 is the equivalent load curve map after optimizing capacity configuration.
Embodiment
Below in conjunction with the accompanying drawings and specific embodiment, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate
The present invention rather than limit 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 equivalents falls within the application appended claims limited range.
Solar energy and wind energy belong to clean reproducible energy, and solar energy power generating and wind-force are all being greatly developed in the whole world
Power generation, will produce large effect, first below to photovoltaic in distributed power generation field to the equivalent load of power distribution network
The power producing characteristics of power generation and wind-power electricity generation are analyzed.
The typical power curve of photovoltaic cell is as shown in Fig. 2, the at a time actual output P of ttIt is represented by：
In formula：TstcIt is the output of photovoltaic panel under standard conditions, corresponding intensity of solar radiation IstcFor 1000W/m2, temperature
Tstc=25 DEG C；IrtFor the intensity of solar radiation of t moment reality, TtFor the temperature of t moment photovoltaic panel.It can be seen that photovoltaic cell
Actual contribute mainly is influenced by intensity of solar radiation and temperature.
As seen from Figure 2 at noon ten two when temperature highest, intensity of illumination is maximum, photovoltaic generating system contribute compared with
Greatly；Morning and evening intensity of illumination is very low, contributes also small.
The wind energy power that wind turbine is sent is represented by：
In formula：W(v)Contribute for wind turbine horizontal, PrFor wind turbine rated power, ρ is atmospheric density, and A is wind wheel sweeping area, V
For wind speed, VrFor rated wind speed, CpFor the power coefficient of wind wheel.
Fig. 3 is certain wind-driven generator power curve on July 20th, 2013, it can be seen that the minimum that this wind-driven generator is contributed
Wind speed is 6m/s, and the wind speed of whole day is all not reaching to rated wind speed, so wind power generation output is to continue the curve of change.Due to
Daytime, especially noon wind speed was relatively low, caused power generation to be contributed at this time substantially zeroed；Since wind speed is larger during the dusk, wind-driven generator
Contribute larger.
Based on the load on July 10th, 2013 in Fig. 1, capacity is chosen respectively and is sent out for 500kW photo-voltaic power supplies and wind-force
Motor, maximum output are calculated by 80%, and the threshold wind velocity of wind-driven generator is 6m/s, and the wind speed under rated power is 15m/s, is stopped
Machine wind speed is 25m/s, and respectively as shown in Figure 4 and Figure 5, the results are shown in Table 1 for peak regulation Contrast on effect for the curve before and after equivalent load.
Table 1：Peak regulation effect compares
It is maximum with reference to above chart it can be found that solar energy power generating power curve is close with the region load curve
Load is greatly reduced, although minimum load has a degree of reduction, peak-valley difference and peak-valley ratio substantially reduce, and peak value is born
The lotus duration greatly increases, peak regulation significant effect.The change of wind power generation output curve is then with load on the contrary, with anti-tune peak
Effect.So solar energy power generating can be used to carry out peak regulation.
From analysis before, the different corresponding peak-valley ratios of load peak character and duration of peaking time have
Difference, needs to optimize its capacity to play the Peak Load Adjustment of distributed generation resource, and while optimizing need to consider it is different
Peak character.
The Peak Load Adjustment of distributed generation resource mainly using its power producing characteristics and part throttle characteristics integrate cut down it is equivalent
Peak load value, therefore effective Peak Load Adjustment is mainly reflected in the change indicator of peak-valley ratio and duration of peaking time
On.With the difference of access distributed generation resource capacity, caused peak-valley ratio reduction degree is also different, with July, 2013
Exemplified by actual loads on the 10th, access in the case of the distributed generation resource of different capabilities, after distributed generation resource output is integrated with load
Equivalent load curve is as shown in Figure 6.As can be seen that with the distributed generation resource capacity increase of access peak-valley ratio can be made to reduce more
It is more, but when distributed generation resource capacity increases to a certain extent, peak-valley ratio will remain unchanged, distributed then as access
The capacity of power supply continues to increase, and peak-valley ratio starts to increase again.Simultaneously as the increase of distributed generation resource capacity is with investment
It can increase.Therefore, it is necessary to consider how to reasonably select the capacity of distributed generation resource.
Under normal circumstances, duration of peaking time was examined according to the duration of more than 90% load of peak load
Consider, the 90% of peak load is also referred to as reference load.As can be seen that when photovoltaic generating system being installed according to 0.6MW, equivalent load
Duration of peaking time be changed into 7 hours from 4 original hours, and load curve becomes more steady, peak load
Duration is longer, and peak regulation effect becomes apparent from.
The present invention counts peak-valley difference according to history daily load curve first when configuration capacity selection is optimized
The distributed area and probability of rate；The relation of power generation, Transmission Cost and capacity is established by electric company's statistics.
Then set up equivalent load curve variance Cf, duration of peaking time rate Cfh, distributed generation resource cost Cc
With the energy-saving benefit C of distributed generation resource accessjThe Model for Multi-Objective Optimization of four indexs.Here, we carry out a definition：I
When carrying out peak-valley ratio and duration of peaking time and calculating analysis, carried out with the load at one day 24 integral point moment
Calculate, the load at our integral point moment is referred to as to compare load.
Initially set up object function：
f1=min (Cf) (5)
Cfh=Δ T/T
In formula, f1For object function one, PiFor the comparison load at each moment, PavFor it is each comparison load average value,
Δ T is duration of peaking time.
Duration of peaking time we can so define：It is horizontal as referring to load using the 90% of peak load, one
Load value at 24 integral point moment is as comparison load in it.When the comparison load at two adjacent moment is big all than reference load
When, then it is assumed that the load within this hour all on reference load, then the duration increase a hour；Work as phase
The comparison load at two adjacent moment is one bigger than reference load, one when be not more than reference load, then duration increasing
Add half an hour；If the comparison load at two moment is all not more than reference load, the duration does not increase.Calculated
When, us are calculated for convenience and is normalized, and specific method is as follows：
When variance and duration of peaking time rate meet condition achieveed the purpose that can be excellent when, we examine again
Consider cost and energy-saving benefit：
f2=min (Cc, Cj) (7)
In formula, CcFor distributed generation resource cost, CjFor the energy-saving benefit of distributed generation resource access, f2After normalization
Object function, (kf+ks) it is unit capacity cost of electricity-generating, Transmission Cost cost coefficient, Δ P is optimizing capacity, and f (Δ P, T) is
Interruption cost, it is related with optimizing capacity and duration,For distributed generation resource investment cost；giFor emission reduction
Amount of carbon dioxide, its unit are ton；ai, bi, ciFor function coefficients, rule of thumb draw；kjjFor unit carbon dioxide emission reduction amount institute
The economic profit coefficients brought, PiFor the comparison load at each moment, k is coefficient, PDGjFor distributed generation resource capacity.
The cost of the present invention is it is contemplated that cost of electricity-generating, Transmission Cost, interruption cost and distributed generation resource
Investment cost carries out the selection that peak value adjusts load.
(1) cost of electricity-generating and Transmission Cost
When often using kilowatt-hour in power distribution network less, then it can delay the certain construction cost of electricity generation system, transmission system.Due to
Power generation expense and transmission charges in current electric company by certain standard, so we can directly use this standard,
It is set to kfAnd ksCalculated, ten thousand yuan/kWh of unit.Cost is directly related to capacity, so as to：
Cf+s=(kf+ks)·ΔP
In formula, Cf+sFor power generation and Transmission Cost.
(2) interruption cost
So-called loss of outage refers to lose caused by national economy since distribution system has a power failure.Including to user
Caused by Custom interruption cost and power department itself because of power failure and caused by economic loss.Because the diversity of power consumer,
Loss of outage assessment is a complicated job, does a simplification to this herein, only considers its economy, as long as thinking load water
Flat to exceed planned load, it will cause loss of outage.
Loss of outage is main related with the duration of capacity and such a capacity level.By chapter 2 content we can
To draw a year based model for load duration curve, according to this curve, as long as determining a capacity level, it is possible to draw a duration,
Further according to the loss of outage of unit capacity under such a capacity, it is possible to calculate when this load level carries out distribution network planning
Wait required interruption cost.
It is contemplated herein that resident's loss of outage calculates, loss of outage influence factor is very much, special with user characteristics, power-off
Property, user type and user are related to the degree of dependence of electric energy.User investigation method is to carry out loss of outage based on user data
The best-evaluated method of calculating, because questionnaire estimation contains many Considerations of the above by inquiry, thus provides us
Calculate the data basis of loss of outage.On the basis of the loss of outage that inquiry agency obtains, calculated, drawn using regression algorithm
Load increases, reduce under interruption cost.
The definition of loss of outage claimable amount ρ (member/(kWh)) is：
In formula, C is interruption cost when power failure 1 is small；D is year power consumption (unit kWh)；T is year electricity consumption hourage
(unit h).
It is X that assumed load, which changes percentage, and loss of outage is Y (units：Member/kWh), by load change percentage with having a power failure
Loss be accordingly to be regarded as stochastic variable, by X and Y correlation coefficient r calculation formula we can obtain：
By result of calculation, we can draw the relation between loss of outage and load change, so that it is determined that load change
Model between loss of outage.
(3) distributed generation resource expense
Introduce distributed generation resource progress peak regulation and necessarily introduce distributed generation resource expense.The investment of distributed generation resource is with dividing
The capacity of cloth power supply is closely related, and this chapter contents are due to the uncertainty of optimizing capacity, so a simplification is done here, it is false
If the investment cost C of distributed generation resourceDGWith distributed generation resource capacity PDGjLinear, wherein k is coefficient：
Next constraints is provided.Because object function is to start to calculate based on peak load level, and when negative
Stop when lotus level is less than the value that some sets, it must is fulfilled for：
Lmin≤L≤Lmax
L is load level in formula, LminIt is a year minimum load, LmaxIt is annual peak load.
PG≤Pmax
P in formulaGIt is distributed generation resource spare capacity, PmaxIt is Peak Load.
%Pimin≤ %Pi≤ %Pimax
%P in formulaiFor peak-valley ratio, %PimaxIt is maximum peak-valley ratio, %PiminIt is minimum peak-valley ratio.
ΔTimin≤ΔTi≤ΔTimax
Δ T in formulaiFor duration of peaking time, Δ TimaxFor peak-peak load duration, Δ TiminFor smallest peaks
The duty value duration.
Due to the load curve in a section peak-valley ratio relatively, its load curve is also more similar, institute
The load curve that day of the internal loading maximum in section can be chosen with us represents load level within this section, and
As initial load.
Then chromosome coding is carried out.Coding is the side for connecting the chromosome of the actual solution and genetic algorithm of problem
Method.Common coding mode has binary coding, symbolic coding and floating-point encoding.In order to simple and convenient, we use real number
Coding.
Table 2：Real coding chromosome
| 2 | 5 | … | … | 3 | 1 |
We are in units of 0.1MW, then first 2 expression 0.2MW, and 5 in form represent 0.5MW, and so on.Dye
Colour solid length we can be provided according to the number in the region that certain city is divided：If dividing 10 regions into, dye
Body length is just 10, is divided into 15 regions, then chromosome length is just 15.So carry out encoding simple and convenient, the speed of service
Also than very fast, the size of connect capacity can be intuitively found out.
Genetic algorithm obtains the search information of next step by solving target function value, and the use of target function value is
Realized by calculating fitness function value size.Concrete operations are first to decode chromosome, then calculate the chromosome
The target function value of corresponding individual, then by target function value fitness is obtained by certain transformation rule.
Be 01 since object function is a positive number certainly, and after being normalized, due to it is required be minimum value, then may be used
Using use 1 and the difference of object function be used as fitness function.F (x)=1-f (x), seeks f (x) minimum values, exactly seeks F (x) most
Big value.
Distributed generation resource capacity is drawn after decoding, at this time with reference to the load curve of peak day, considers the access sun
Equivalent load curve after energy photo-voltaic power supply, judges whether constraints at this time meets.
The evolutionary process of nature follows the principle of " survival of the fittest ", i.e., species high to adaptive capacity to environment will survive
Get off, procreation is of future generation；And to be genetic to follow-on possibility just small for the low species of adaptability, is slowly eliminated.Heredity is calculated
Selection opertor in method is exactly that the process for simulating this survival of the fittest is realized.
The selection opertor that the present invention uses uses optimum maintaining strategy, i.e., when operation is made choice, it is contemplated that
The highest individual of filial generation fitness function is directly entered the next generation, namely ensures most have individual to enter the next generation per a generation
Continue genetic manipulation.
Crossover operation in genetic algorithm refers to according to certain rules carry out the portion gene on the chromosome of two pairings
Exchange and obtain two new individuals.Crossover operation plays an important role in genetic algorithm, is the main method for producing new individual.This
The crossover operator of text is intersected using single-point, first carries out random pair two-by-two to the individual in colony.Matched to every a pair
Body, generates crosspoint at random, then according to the portion gene on crossover probability chiasmatypy.
Mutation operation in genetic algorithm is that other allele are replaced to some or some genes of chromosome, and then
New individual is obtained, the mutation operation that the present invention carries out is as follows：
To each gene position of individual, change point is specified according to mutation probability；According to right on current chromosome change point
The specific numbering generation field solution answered, determines candidate solution.Assuming that initial solution and change point situation are as shown in table 3：
Table 3：Mutation operation
| 0 | 1 | 6 | 3 | 5 |
Assuming that there are 6 regions in somewhere, then the gene position value on chromosome is 0-6, and somewhere will access 0.5MW capacity
Distributed generation resource, calculated by a unit of 0.1MW, then we can draw chromosome length be 5, such as table 3-7 institutes
Show, it is assumed that the gene position that genic value is 6 is change point, then can produce field solution, disaggregation is (0 103 5) (0 113
5) (0 123 5) (0 133 5) (0 143 5) (0 153 5), then to select neighborhood solution to concentrate optimal individual.
It is when iterations reaches maximum iteration to search for end condition.Whole flows are as shown in Figure 8.
Next with an example come illustrate the present invention be how utilize genetic algorithm carry out distributed generation resource capacity optimization
's.In this Section combines certain city's distribution network planning content, chooses 10 sub-regions conducts of division in the high speed collar region in certain city
Research object, does not have industrial user in circle at a high speed, it is assumed that 10 sub-regions uniform illuminations in circle at a high speed, temperature is the same, chooses peak
The morning peak situation higher than evening peak is analyzed in characteristic.Calculated by collecting, it can be found that having within 1 year 266 days in circle at a high speed
With this peak character, the load curve with such peak character is divided according to peak-valley ratio, and is divided in five areas
Between in, it is specific as shown in table 4.Due to the load curve in a section peak-valley ratio relatively, its load curve
It is more similar, so the load curve that we can choose that day of the internal loading maximum in section is represented within this section
Load level.
Table 4：The probability distribution of peak-valley ratio
| Peak-valley ratio | 0-20% | 20%-40% | 40%-60% | 60%-80% | 80%-100% |
| Probability | 0% | 10.15% | 55.64% | 22.94% | 11.27% |
Loss of outage data in our bibliography [48] of the data of loss of outage reparation model, carry out loss of outage mould
The calculating of type.
Table 5：Typical user's loss of outage statistical form
| Load change percentage (compared with maximum load) | Resident's loss of outage claimable amount (member/kWh) |
| 0 | 6.4,9,7.1 |
| - 10% | 7.1,8.9,10 |
| - 20% | 9,9.5,10.7 |
| - 30% | 11,12.3,14 |
Resident's correlation coefficient r can be calculated according to the calculation formula of foregoing correlation coefficient rg=0.742.Inquiry is related
Coefficient form is it is recognised that there are certain linear relationship between loss of outage and load change percentage, so we can be with
Using load change as independent variable, loss of outage is independent variable, establishes linear regression model (LRM)：
Y=a+bX
The parameter that resident can be obtained respectively according to table 5 is：aj=1.155bj=1.744；
The relational expression that can be obtained again between resident's loss of outage and Load Regulation is：
Yj=1.155+1.744Xg(-0.3EXgE)
Since load and duration are according to year based model for load duration curve come as defined in, we can be straight when calculating
Connect from curve and draw.When energy-saving benefit calculating is carried out, it is contemplated that using power and the mould of carbon emission in document [49]
Type is calculated.
Initial parameter sets as follows：Population Size is set to 100, we are chosen for 1000 to iterations N, if crossover probability
Too big then result just has certain randomness, is restrained if too small relatively slow, we choose 0.45 herein, and mutation probability generally exists
Between 0.01 to 0.1, we elect 0.06 as herein.It is new according to certain electric company 12 period 110kV and following specific investment cost
Increase load according to 0.003kW/ members to be calculated, then can reduce 110kV and to issue the power grid construction capital cost of transmission facility
With 2.51 hundred million yuan, that is to say, that we can regard hair transmission of electricity as one entirety, then power distribution network often saves the electricity of 1kW, kf+ks=
0.033 (ten thousand yuan/kW), when peak-valley ratio calculating is carried out, it is equivalent to consider that access solar photovoltaic power obtains afterwards
Load, a bodge are 0.1MW.
Due to needing the situation for meeting peak load when carrying out distribution network planning, we combine yearly load curve first
Annual peak load is drawn in this day on the 20th of August in 2013, the peak-valley ratio of this day is 59%, is fallen in 40%60% this area
Between within, so the load curve for choosing this day represents all load curves within this section.When genetic algorithm terminates
When, we can show that the distributed generation resource Peak Load in 10 regions is respectively：
Table 6：Distributed generation resource Peak Load distribution table
| Numbering | A | B | C | D | E | F | G | H | I | J |
| It is optimal by variance | 3 | 2 | 0 | 2 | 1 | 2 | 10 | 1 | 1 | 4 |
| By economic optimum | 2 | 3 | 1 | 1 | 3 | 1 | 8 | 2 | 1 | 3 |
Since the probability that peak-valley ratio occurs between 0-20% is 0, so we are without considering this section situation.By
This, we can show that the distributed generation resource that different peak-valley ratios are drawn distributes capacity rationally respectively：
Table 7：Different peak-valley ratio optimizing capacity configurations (optimal by variance)
| Numbering | A | B | C | D | E | F | G | H | I | J |
| 20%-40% | 1 | 2 | 1 | 2 | 0 | 3 | 8 | 2 | 1 | 1 |
| 40%-60% | 2 | 2 | 0 | 2 | 1 | 2 | 9 | 1 | 1 | 3 |
| 60%-80% | 1 | 3 | 2 | 4 | 2 | 1 | 7 | 2 | 3 | 1 |
| 80%-100% | 2 | 1 | 1 | 2 | 4 | 3 | 8 | 3 | 1 | 2 |
So with reference to the probability in each peak-valley ratio place section, we can draw according to table 7 and be obtained according to variance is optimal
What is gone out distributes capacity rationally, as shown in table 8.
Table 8：Final optimization pass configuration capacity (optimal by variance)
| Numbering | A | B | C | D | E | F | G | H | I | J |
| Capacity | 2 | 2 | 1 | 2 | 1 | 2 | 8 | 2 | 2 | 3 |
Table 9：Different peak-valley ratio optimizing capacities configure (economy is more excellent)
| Numbering | A | B | C | D | E | F | G | H | I | J |
| 20%-40% | 0 | 1 | 2 | 2 | 1 | 2 | 8 | 3 | 2 | 1 |
| 40%-60% | 1 | 2 | 0 | 2 | 1 | 1 | 9 | 2 | 1 | 2 |
| 60%-80% | 2 | 4 | 3 | 2 | 2 | 2 | 7 | 2 | 2 | 1 |
| 80%-100% | 2 | 2 | 3 | 1 | 2 | 3 | 8 | 3 | 1 | 3 |
Similarly we can draw the optimal scheme of the economy drawn on the basis of meeting variance preferably, such as table 9
Shown in table 10.
By result of calculation, we are by taking the load condition of peak day as an example, it can be deduced that distributed photovoltaic power is contributed
The equivalent load situation of situation and load, as shown in Figure 9.
From figure it will be seen that after access solar photovoltaic power, it is by the more excellent peak-valley ratio that draws of variance
38.7%, when duration of peaking time is 6.2 small, economic benefit is 223.2 ten thousand yuan；Peak-valley difference is obtained according to economy is more excellent
Rate is 40%, when duration of peaking time is 5.9 small；Economic benefit is 231.9 ten thousand yuan.We choose variance most for synthesis
Capacity configuration is optimized in the case of excellent.The situation in peak load level 81% is understood by the optimization of distributed generation resource capacity
Under, that is, in the case that the duration is 700h, it can obtain comprehensive best distributing effect rationally at this time.
Table 10：Final optimization pass configuration capacity (economy is more excellent)
| Numbering | A | B | C | D | E | F | G | H | I | J |
| Capacity | 1 | 2 | 1 | 3 | 1 | 2 | 8 | 2 | 1 | 2 |
Table 11：Optimum results compare
| It is optimal by peak regulation effect | It is optimal by economy | |
| Capacity (MW) | 2.5 | 2.3 |
| Power generation and Transmission Cost (ten thousand yuan) | 657.2 | 608.1 |
| Interruption cost (ten thousand yuan) | 201.4 | 135.2 |
| Distributed generation resource investment cost (ten thousand yuan) | 300 | 276 |
| Energy-saving benefit (ten thousand yuan) | 66.4 | 35.0 |
| Total economic benefit (ten thousand yuan) | 223.2 | 231.9 |
Claims (7)
1. a kind of distributed generation resource peak capacity optimization method, it is characterised in that comprise the following steps：
1) distributed area and probability of peak-valley ratio are counted according to historical data, and establishes power generation, the cost of transmission of electricity and capacity
Between relation；
2) variance of each Time comparison load is normalized with duration of peaking time, show that object function is expressed
Formula；
3) load curve of that day where peak load point in the section is selected in each peak-valley ratio section as initial
Load level；
4) chromosome coding is carried out, produces initial population；
5) fitness is calculated, and carries out the comparison of fitness, retains larger fitness value；
6) judge whether to meet end condition, the jump procedure 7 if meeting)；Otherwise start to select, intersect, make a variation and jump to
Step 5)；
7) draw optimum population, be compared in colony, respectively output variance it is minimum and two best sides of economic benefit
Case.
2. distributed generation resource peak capacity optimization method according to claim 1, it is characterised in that return in the step 2)
Obtained object function is after one change processing：
f2=min (Cc, Cj)
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In formula, CcFor distributed generation resource cost, CjFor the energy-saving benefit of distributed generation resource access, f2For the mesh after normalization
Scalar functions, (kf+ks) it is unit capacity cost of electricity-generating, Transmission Cost cost coefficient, Δ P is optimizing capacity, and f (Δ P, T) is power failure
Failure costs, it is related with optimizing capacity and duration,For distributed generation resource investment cost；giFor the dioxy of emission reduction
Change carbon amounts, its unit is ton；ai, bi, ciFor function coefficients, rule of thumb draw；kjjBrought by unit carbon dioxide emission reduction amount
Economic profit coefficients, PiFor the comparison load at each moment, k is coefficient, PDGjFor distributed generation resource capacity.
3. distributed generation resource peak capacity optimization method according to claim 2, it is characterised in that the step 2) is also wrapped
Include following constraints：
Lmin≤L≤Lmax,
L is load level in formula, LminIt is a year minimum load, Lmax, it is annual peak load；
PG≤Pmax,
P in formulaGIt is distributed generation resource spare capacity, Pmax, it is Peak Load；
%Pimin≤ %Pi≤ %Pimax
%P in formulaiFor peak-valley ratio, %PimaxIt is maximum peak-valley ratio, %PiminIt is minimum peak-valley ratio；
ΔTimin≤ΔTi≤ΔTimax
Δ T in formulaiFor duration of peaking time, Δ TimaxFor peak-peak load duration, Δ TiminBorn for minimum peak
The lotus duration.
4. distributed generation resource peak capacity optimization method according to claim 1, it is characterised in that dye in the step 4)
Colour solid coding uses real coding, and in units of 0.1MW, chromosome length is equal to power distribution network division number of regions.
5. distributed generation resource peak capacity optimization method according to claim 1, it is characterised in that in the step 6)
End condition is that iterations reaches maximum iteration.
6. distributed generation resource peak capacity optimization method according to claim 1, it is characterised in that institute in the step 6)
State selected as optimum maintaining strategy.
7. distributed generation resource peak capacity optimization method according to claim 1, it is characterised in that institute in the step 6)
Intersection is stated for single-point to intersect.
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