CN103077316A - Peak-clipping and valley-filling optimizing method of load curve - Google Patents

Abstract

The invention provides a peak-clipping and valley-filling optimizing method of a load curve, and relates to a method for optimizing a peak-clipping and valley-filling effect. The method comprises the following steps of establishing a peak-clipping and valley-filling optimizing model; and solving the peak-clipping and valley-filling optimizing model, so as to obtain the optimum peak load and peak-valley difference. The method has the advantages that the defects of subjectivity and only one solution by one-time optimizing in the traditional peak-clipping and valley-filling optimizing method are overcome; a plurality of groups of peak loads and peak-valley differences can be solved, so a user can objectively select the optimum peak load and peak-valley difference according to actual requirements; and compared with the traditional algorithm, the better optimizing effect can be ensured, the heavy pressure generated by peak adjusting and frequency adjusting is avoided, and the power qualities, such as voltage and frequency, are improved.

Description

A kind of peak load shifting optimization method of load curve

Technical field

The present invention relates to the peak load shifting effect is optimized, particularly a kind of peak load shifting optimization method of load curve.

Background technology

Present stage, the Important Problems about peak load shifting research also was simultaneously that hot issue is a kind of optimization method that can reduce simultaneously the peak load, reduce peak-valley difference of how to confirm, and this problem is a multi-objective optimization question that typically has multivariate, multi-constraint condition.

The peak load shifting Multipurpose Optimal Method also is confined to traditional multiple-objection optimization disposal route at present.Traditional Multipurpose Optimal Method has the method for weighting, leash law and Objective Programming etc., and multi-objective optimization question is converted into single-object problem, then utilizes the method for finding the solution single-object problem to obtain a solution of problem.Document [1] proposes smallest peaks load, minimum peak-valley difference and user satisfaction are made as optimization aim, and these three targets are arranged weight, multi-objective problem is converted into the single goal problem finds the solution; Document [2] is to smallest peaks load, when minimum peak-valley difference multi-objective optimization question is analyzed, although adopting fuzzy membership function processes optimization aim, but adopted in the committed step of optimizing and to have converted optimization aim to the constraint condition method, multi-objective optimization question is converted into single-object problem analysis, and it still belongs to traditional Multipurpose Optimal Method in essence.

The inventor finds to exist at least in the prior art following shortcoming and defect in realizing process of the present invention:

The weights of each objective function of traditional algorithm are artificial regulations, usually obtain a kind of peak load shifting effect of optimization, but the method have larger subjectivity.In view of China take thermal power generation as main, the general layout that the peak-frequency regulation ability is relatively poor, the bad meeting of peak load shifting effect brings immense pressure for peak-frequency regulation, has a strong impact on the qualities of power supply such as voltage, frequency.

Summary of the invention

The invention provides a kind of peak load shifting optimization method of load curve, this method gets access to the peak load shifting effect after the multiple optimization, has avoided peak-frequency regulation is produced immense pressure, has improved the qualities of power supply such as voltage, frequency, sees for details hereinafter and describes:

A kind of peak load shifting optimization method of load curve said method comprising the steps of:

(1) sets up the peak load shifting Optimized model;

(2) described peak load shifting Optimized model is found the solution, obtain optimum peak load and peak-valley difference;

Described peak load shifting Optimized model is specially:

$\left\{\begin{array}{cc}{Q}_i^{\′}=Q_{i}f\left(P_f\right)& i\∈T_f\\ Q_j^{\′}=Q_{j}f\left(P_g\right)& j\∈T_g\\ Q_k^{\′}=Q_{k}f\left(P_p\right)+[\underset{i}{\mathrm{\Σ}}(Q_i^{\′}-Q_i)+\underset{j}{\mathrm{\Σ}}(Q_j^{\′}-Q_j)+\underset{k}{\mathrm{\Σ}}(Q_{k}f\left(P_p\right)-Q_k)]/n& k\∈T_p\end{array}\right.$

Wherein, P _f, P _p, P _gIt is one group of electricity price; T _f, T _g, T _pExpression peak, paddy, section at ordinary times; Q _i, Q _j, Q _kExpression peak load shifting leading peak, paddy, the power consumption of section at ordinary times; Expression peak load shifting postpeak, paddy, the power consumption of section at ordinary times; N be at ordinary times the section hourage;

The optimization aim function

(a) peak load value Q _MaxMinimize:

min(Q _max)=min{max[Q(P _f,P _p,P _g,T _f,T _p,T _g)]}

(b) the poor Q of peak load _Max-Q _MinMinimize:

min(Q _max-Q _min)=min{max[Q(P _f,P _p,P _g,T _f,T _p,T _g)]-min[Q(P _f,P _p,P _g,T _f,T _p,T _g)]}

Wherein, Q (P _f, P _p, P _g, T _f, T _p, T _g) for after implementing tou power price, the load value of power consumer after according to customer responsiveness curve peak load shifting;

Constraint condition

(a) implementing tou power price front and back total electricity consumption remains unchanged:

Q=Q _f+Q _p+Q _g=Q _fTOU+Q _pTOU+Q _gTOU

Wherein, Q _f, Q _p, Q _gFor implementing tou power price leading peak, flat, paddy period power consumption; Q _FTOU, Q _PTOU, Q _GTOUFor implementing tou power price postpeak, flat, paddy period power consumption;

(b) electricity price mobility scale constraint:

P _fmin≤P _f≤P _fmax

P _pmin≤P _p≤P _pmax

P _gmin≤P _g≤P _gmax

Wherein, P _Fmin, P _Fmax, P _Pmin, P _Pmax, P _Gmin, P _GmaxConcrete numerical value for peak, flat, paddy day part electricity price change bound;

(c) economic target constraint:

(1-δ)M ₀<Q _fTOUP _f+Q _pTOUP _p+Q _gTOUP _g<M ₀

Wherein, δ=M '/M ₀Be the interest concessions coefficient; M ₀Be the total electricity charge of user before the implementation tou power price; M ' is the power cost that power supply department is saved behind the enforcement tou power price.

Described described peak load shifting Optimized model is found the solution, obtains optimum peak load and peak-valley difference and specifically comprise:

(1) actual peak, flat, paddy electricity price are carried out Code And Decode; In the scope of day part electricity price change, produce initial population;

(2) with the objective function of all individual respectively substitution peak loads of current population and peak-valley difference, obtain each individual each corresponding target function value; According to described each target function value described current population is carried out quick non-dominated Sorting, and calculate crowding distance;

(3) calculate virtual fitness by ranking results and described crowding distance, and carry out individual choice, crossover and mutation computing; To application of results elitism strategy after the computing, obtain new progeny population;

(4) take described new progeny population as the basis, repeated execution of steps (2)-(3) until reach maximum iteration time, obtain many groups peak load and peak-valley difference;

(5) from described many groups peak load and peak-valley difference, obtain described optimum peak load and peak-valley difference.

Described actual peak, flat, paddy electricity price carried out Code And Decode and be specially:

With the industry average electricity price P before the implementation tou power price ₀Be reference value, to peak, flat, paddy period electricity price P _f, P _p, P _gCarry out the perunit value reduction, coded format is: [x _f, x _p, x _g]; Actual peak, paddy, the ordinary telegram valency is corresponding is decoded as:

$\left\{\begin{array}{c}{P}_f=x_f{P}_0\\ P_p=x_p{P}_0\\ P_g=x_{\mathrm{<figure-callout\; id="0"\; label="g\; P"\; filenames="HDA00002734579700011.png"\; state="\{\{state\}\}">g</figure-callout>}}{P}_{}{}_0\end{array}\right..$

Describedly obtain from described many groups peak load and peak-valley difference that load in described optimum peak and peak-valley difference is specially:

From described many groups peak load and peak-valley difference, directly choose described optimum peak load and peak-valley difference according to preset need; Or,

Bring described many groups peak load and peak-valley difference into fuzzy membership function, choose described optimum peak load and peak-valley difference.

Describedly bring described many groups peak load and peak-valley difference into fuzzy membership function, choose that load in described optimum peak and peak-valley difference specifically comprises:

Described fuzzy membership function h _iBe specially:

$h_i=\left\{\begin{array}{cc}1& f_i\&le;f_{i\min }\\ \frac{f_{i\max }-f_i}{f_{i\max }-f_{i\min }}& f_{i\min }<f_i<f_{i\max }\\ 0& f_i\&GreaterEqual;f_{i\max }\end{array}\right.$

Wherein, f _iBe peak load or peak-valley difference, i=1,2; f _IminAnd f _ImaxBe respectively maximal value and the minimum value of peak load or peak-valley difference;

Adopt again h=h ₁+ h ₂, corresponding described optimum peak load and peak-valley difference when selecting the h maximum.

The beneficial effect of technical scheme provided by the invention is: by setting up the peak load shifting Optimized model; The peak load shifting Optimized model is found the solution, get access to optimum peak load and peak-valley difference.The subjectivity of traditional peak load shifting optimization and the shortcoming that a suboptimization can only obtain a solution have been overcome; By obtaining many groups peak load and peak-valley difference, so that the user can select optimum peak load and peak-valley difference according to actual needs objectively, can guarantee to get better effect of optimization with respect to traditional algorithm, avoid peak-frequency regulation is produced immense pressure, improve the qualities of power supply such as voltage, frequency.

Description of drawings

Fig. 1 is the synoptic diagram of local congestion distance;

Fig. 2 is the multiple goal peak load shifting Optimizing Flow figure based on the NSGA-II algorithm;

Fig. 3 is many groups peak load of obtaining based on the NSGA-II algorithm and the synoptic diagram of peak-valley difference;

Fig. 4 is a kind of process flow diagram of peak load shifting optimization method of load curve.

Embodiment

For making the purpose, technical solutions and advantages of the present invention clearer, embodiment of the present invention is described further in detail below in conjunction with accompanying drawing.

In order to get access to the peak load shifting effect after the multiple optimization, avoid peak-frequency regulation is produced immense pressure, improve the qualities of power supply such as voltage, frequency, the embodiment of the invention provides a kind of peak load shifting optimization method of load curve.

Mostly connect each other, restrict between each target of multi-objective optimization question, in addition conflicting, be difficult to find an optimum solution truly so that each target reaches optimum simultaneously.So when carrying out multiple-objection optimization, generally do not have " unique optimum solution ", but have the set of a plurality of " satisfactory solutions ", be commonly referred to as the Pareto optimal solution set.NSGA-II[3] be a kind of novel multi-objective Optimization Genetic Algorithm, the Pareto optimum solution that this algorithm obtains is evenly distributed at object space, and convergence and robustness are good, and the NSGA-II algorithm can overcome all shortcomings of traditional algorithm multiple-objection optimization.This method in peak load shifting optimization, has been set up peak load shifting multiple-objection optimization mathematical model with the NSGA-II algorithm application, referring to Fig. 2 and Fig. 4, sees for details hereinafter and describes:

101: set up the peak load shifting Optimized model;

As independent variable x, user's power load under this electricity price is dependent variable y, can form the user to the response curve of electricity price with electricity price.The independent variable of this curve and dependent variable generally be industry average electricity price and the power consumption carried out before the Peak-valley TOU power price be that the perunit value that the reference value conversion forms represents.By certain industry power consumer is investigated and analysed, obtain the power load value of user under a plurality of different electricity prices, again the data that obtain are carried out match, can obtain customer responsiveness curve y=f (x).

The known period is divided into T _f, T _p, T _g(T _f+ T _p+ T _g=24h); Power consumer typical case daily load curve is known; Guaranteeing that power consumer is made a response according to electricity price response curve y=f (x) under the prerequisite that user's total electricity consumption remains unchanged before and after the tou power price peak load shifting, realize the peak load shifting effect, Optimized model is as follows:

(1) Computing Principle

At a certain group of electricity price P _f, P _p, P _gLoad curve behind the lower peak load shifting can be obtained by following formula:

$\left\{\begin{array}{cc}{Q}_i^{\&prime;}=Q_{i}f\left(P_f\right)& i\&Element;T_f\\ Q_j^{\&prime;}=Q_{j}f\left(P_g\right)& j\&Element;T_g\\ Q_k^{\&prime;}=Q_{k}f\left(P_p\right)+[\underset{i}{\mathrm{\&Sigma;}}(Q_i^{\&prime;}-Q_i)+\underset{j}{\mathrm{\&Sigma;}}(Q_j^{\&prime;}-Q_j)+\underset{k}{\mathrm{\&Sigma;}}(Q_{k}f\left(P_p\right)-Q_k)\&rsqb;/n& k\&Element;T_p\end{array}\right.$

Q _i, Q _j, Q _kExpression peak load shifting leading peak, paddy, the power consumption of section at ordinary times;

Expression peak load shifting postpeak, paddy, the power consumption of section at ordinary times; N be at ordinary times the section hourage; F (P _f) represent P _fBe brought among the electricity price response curve y=f (x), by that analogy.The first formula represents the peak clipping process of load curve; The second formula represents the paddy process of filling out of load curve; The 3rd formula represents behind the peak load shifting to be averagely allocated to the difference of total load and former total load at ordinary times, and section remains unchanged with user's total electricity consumption before and after satisfying peak load shifting.

(2) optimization aim

(a) peak load value Q _MaxMinimize:

min(Q _max)=min{max[Q(P _f,P _p,P _g,T _f,T _p,T _g)]}（1）

(b) the poor Q of peak load _Max-Q _MinMinimize:

min(Q _max-Q _min)=min{max[Q(P _f,P _p,P _g,T _f,T _p,T _g)]-min[Q(P _f,P _p,P _g,T _f,T _p,T _g)]}（2）

Wherein: Q _MaxBe the maximum peak load value; Q _MinBe the minimum valley load value; Q (P _f, P _p, P _g, T _f, T _p, T _g) for after implementing tou power price, the load value of power consumer after according to customer responsiveness curve peak load shifting.

(3) constraint condition

(a) implementing tou power price front and back total electricity consumption remains unchanged:

Q=Q _f+Q _p+Q _g=Q _fTOU+Q _pTOU+Q _gTOU（3）

Wherein: Q _f, Q _p, Q _gFor implementing tou power price leading peak, flat, paddy period power consumption; Q _FTOU, Q _PTOU, Q _GTOUFor implementing tou power price postpeak, flat, paddy period power consumption.

(b) electricity price mobility scale constraint:

P _fmin≤P _f≤P _fmax（4）

P _pmin≤P _p≤P _pmax（5）

P _gmin≤P _g≤P _gmax（6）

Wherein: P _Fmin, P _Fmax, P _Pmin, P _Pmax, P _Gmin, P _GmaxBe the concrete numerical value of peak, flat, paddy day part electricity price change bound, this concrete numerical value determines that by the supervision department in the practical application etc. the embodiment of the invention does not limit this.

(c) economic target constraint:

(1-δ)M ₀<Q _fTOUP _f+Q _pTOUP _p+Q _gTOUP _g<M ₀（7）

Wherein: δ=M '/M ₀Be interest concessions coefficient, General Requirements 0＜δ＜10%; M ₀Be the total electricity charge of user before the implementation tou power price; M ' is the power cost that power supply department is saved behind the enforcement tou power price.

102: the peak load shifting Optimized model is found the solution, obtain optimum peak load and peak-valley difference.

This method refers to the NSGA-II algorithm in the optimization of multiple goal tou power price, and this step specifically comprises:

(1) actual peak, paddy, ordinary telegram valency are carried out Code And Decode;

With the industry average electricity price P before the implementation tou power price ₀Be reference value, to peak, flat, paddy period electricity price P _f, P _p, P _gCarry out the perunit value reduction, with the perunit value that obtains as decision variable with x _f, x _p, x _gExpression.Select the mode of real coding, can get the decision variable coded format and be: [x _f, x _p, x _g]; Actual peak, flat, the corresponding coding/decoding method of paddy electricity price are:

$\left\{\begin{array}{c}{P}_f=x_f{P}_0\\ P_p=x_p{P}_0\\ P_g=x_g{P}_0\end{array}\right.---\left(8\right)$

(2) in the scope of peak, flat, paddy day part electricity price change, produce initial population;

Bear psychology according to production cost and user, the restriction range of local electricity price is: P _Fmin≤ P _f≤ P _Fmax, P _Pmin≤ P _p≤ P _Pmax, P _Gmin≤ P _g≤ P _GmaxP _Fmin, P _Fmax, P _Pmin, P _Pmax, P _Gmin, P _GmaxThe concrete numerical value of peak, flat, paddy day part electricity price change bound.Random initializtion population X in this scope _i=[x _Fi, x _Pi, x _Gi] i=1,2 ... N, N are Population Size; X _iBe any individual in the population; [x _Fi, x _Pi, x _Gi] be individual X _iThe gene phenotype.

(3) with all individual respectively substitution formulas (1) of current population and the peak load of formula (2) expression and the objective function of peak-valley difference, obtain each individual each corresponding target function value;

(4) according to each target function value that obtains current population is carried out quick non-dominated Sorting, and calculate crowding distance;

Before selecting, according to the noninferior solution level of individuality to current population layering.This algorithm need to calculate two the parameter n of each individual i in the current population _iAnd S _i, n wherein _iBe the number of individuals of the individual i of domination in the current population, S _iBe the individual collections of being arranged by individual i in the current population.Wherein, the concrete steps of quick non-dominated Sorting are conventionally known to one of skill in the art, and the embodiment of the invention is not done this and given unnecessary details.

(5) calculate virtual fitness by ranking results and crowding distance;

In the NSGA-II algorithm, individual fitness comprises non-domination order i _RankVirtual fitness i with individuality _dIn order to keep individual diversity, to prevent individually in local accumulation, NSGA-II realizes description to virtual fitness with crowding distance, the local congestion distance between adjacent with ad eundem 2 of the every bit on its concrete feeling the pulse with the finger-tip mark space.For example among Fig. 1, f ₁, f ₂Be two targets of optimization problem, i.e. peak load and peak-valley difference.The crowding distance that object space i is ordered equals 2 length of side sums of rectangle that it forms at the adjacent some i-1 of same grade and i+1.This method can make result of calculation scatter more equably at object space, has good robustness.During specific implementation, then the chromosome of at first decoding press each individual corresponding target function value of peak load shifting seismic responses calculated, carries out non-bad layering according to target function value again, calculates the virtual fitness of every layer of individuality.This step is conventionally known to one of skill in the art, and the embodiment of the invention is not done at this and given unnecessary details.

(6) carry out individual choice, crossover and mutation computing according to virtual fitness;

Selection course makes to optimize towards the direction of Pareto optimal solution set carries out and makes the solution uniformly dispersing.The selection operator is the loss for fear of effective gene, makes high performance individuality with larger probability existence, thereby improves global convergence and counting yield.Calculate through ordering and crowding distance, the individual i of each in the colony obtains two attributes: non-domination order i _RankWith crowding distance i _dCrossover and mutation cooperatively interacts and can make genetic algorithm have good part and global search performance, this method adopts " binary championship " to select operator, SBX crossover operator and polynomial expression mutation operator to carry out computing, can also adopt other compute mode, during specific implementation, the embodiment of the invention does not limit this.

(7) to application of results elitism strategy after the computing, obtain new progeny population Q _T+1

The defect individual that elitism strategy namely keeps in the parent directly enters filial generation, and it is the necessary condition of genetic algorithm convergence with probability 1.This step is conventionally known to one of skill in the art, and the embodiment of the invention is not done this and given unnecessary details.

(8) with new progeny population Q _T+1Be the basis, repeated execution of steps (3)-(7) until reach maximum iteration time, obtain many groups peak load and peak-valley difference;

Wherein, maximum iteration time is set according to the needs in the practical application, and during specific implementation, the embodiment of the invention does not limit this.After this step is finished, can obtain the Pareto forward position after NSGA-II optimizes, namely many group peak loads and peak-valley difference.

(9) from many groups peak load and peak-valley difference, obtain optimum peak load and peak-valley difference.

Wherein, this step is specially: directly choose optimum peak load and peak-valley difference according to preset need from many groups peak load and peak-valley difference; Or, will organize peak load and peak-valley difference more and bring fuzzy membership function into, choose optimum peak load and peak-valley difference.

Wherein, fuzzy membership function is specially:

$h_i=\left\{\begin{array}{cc}1& f_i\&le;f_{i\min }\\ \frac{f_{i\max }-f_i}{f_{i\max }-f_{i\min }}& f_{i\min }<f_i<f_{i\max }\\ 0& f_i\&GreaterEqual;f_{i\max }\end{array}---\left(9\right)\right.$

Wherein: f _iBe peak load or peak-valley difference, i=1,2; f _IminAnd f _ImaxBe respectively maximal value and the minimum value of peak load or peak-valley difference.

Adopt again following formula

h=h ₁+h ₂（10）

Corresponding optimum peak load and peak-valley difference when selecting the h maximum.

The below illustrates the feasibility of the peak load shifting optimization method of a kind of load curve that the embodiment of the invention provides with a concrete example, sees for details hereinafter to describe:

(1) example basic condition

(a) the large industrial user typical case in somewhere daily load data:

The large industrial user's original loads data of table 1

(b) the large industrial user's average electricity price in this area is: P ₀=0.5647 yuan/kWh.

(c) large industrial user purchases electricity charge usefulness: M ₀=8.80 * 10 ⁶Unit.

(d) period splitting scheme:

Load period: 08:00～12:00(peak 1, peak); 18:00～22:00(peak 2).

Flat load period: 12:00～18:00(flat 1); 22:00～24:00(flat 2).

Paddy load period: 00:00～04:00(paddy 1); 04:00～08:00(paddy 2).

(e) user's electricity price response curve:

Y=f (x)=-0.2807x+1.2952, x is the electricity price perunit value, y is user power utilization amount perunit value.

(f) peak, flat, paddy electricity price restriction range:

0.3≤x _g≤0.8

0.8≤x _p≤1.2

1.2≤x _f≤1.8

(2) the invention process step

Take (1), (2) formula as objective function, take (3), (4), (5), (6) as constraint condition, can set up based on NSGA-II tou power price Model for Multi-Objective Optimization as follows:

$\left\{\begin{array}{c}\min \left(Q_{\max }\right)=\min \{\max \&lsqb;Q(P_f,P_p,P_g,T_f,T_p,T_g)\&rsqb;\}\\ \min (Q_{\max }-Q_{\min })=\min \{\max \&lsqb;Q(P_f,P_p,P_g,T_f,T_p,T_g)\&rsqb;-\min \&lsqb;Q(P_f,P_p,P_g,T_f,T_p,T_g)\&rsqb;\}\end{array}\right.$

$s.t.\left\{\begin{array}{c}{Q}_f^{\&prime;}+Q_p^{\&prime;}+Q_g^{\&prime;}=Q_f+Q_p+Q_g\\ P_{f\min }\&le;P_f\&le;P_{f\max }\\ P_{p\min }\&le;P_p\&le;P_{p\max }\\ P_{g\min }\&le;P_g\&le;P_{g\max }\end{array}\right.$

Utilize NSGA-II to pass through step in the technical scheme (1)～(8) and can obtain many groups peak load and peak-valley difference, can obtain optimum peak load and peak-valley difference by step (9) again.

(3) the invention process result

(a) the many groups peak load and the peak-valley difference that obtain after the optimization:

(b) optimal compromise solution:

x _f=1.4054；x _p=1.1697；x _g=0.4010。

Can get after the decoding:

Peak period electricity price: 0.7936 yuan/kWh;

At ordinary times the section electricity price: 0.6605 yuan/kWh;

Paddy period electricity price: 0.2264 yuan/kWh.

(c) optimum peak load and peak-valley difference:

Peak load: 70.64 ten thousand kW; Peak-valley difference 14.36 ten thousand kW.

(d) the interest concessions coefficient is δ=0.16%, satisfies inequality constrain condition (7).

(e) optimum results is showed:

Load characteristic value comparable situation before and after optimizing is as shown in table 2, and load curve contrasts situation as shown in Figure 3:

The part throttle characteristics that table 2 is used before and after the NSGA-II optimization compares

(4) the invention process result and traditional weighting Multipurpose Optimal Method contrast

(a) use NSGA-II and can obtain a plurality of objective solutions, and classic method can only obtain subjective a solution.

Table 3 is that one group of representative solution that the Pareto forward position obtains from Fig. 3 can be found out by Fig. 3 and table 3, " the peak load is minimum " and " peak-valley difference is minimum " these two targets are conflicting, can not find one to make the simultaneously solution of optimum of two targets, therefore can only select according to actual needs, when the decision maker, can select in the Pareto forward position of Fig. 3 upper left (for example the 1st group of solution of table 3) during as main target take peak load minimum in two targets; With should the decision maker during take the peak-valley difference minimum as main target, can select in Pareto disaggregation lower right-most portion (for example the 5th group of solution of table 3).If when the decision maker does not have to specify the objective function of laying particular stress on especially, can get optimal compromise solution (the 3rd group of solution in the table 3, point shown in Fig. 3) as optimum results.

Table 3 part peak load and peak-valley difference

(b) use NSGA-II with respect to traditional algorithm and can guarantee to get better effect of optimization

Utilize this Simulation Example experiment to prove, when with traditional multiple-objection optimization, if Weight selected is improper, also will cause peak load and peak-valley difference all greater than optimum peak load and the peak-valley difference of (being inferior to) NSGA-II algorithm, the contrast situation is as shown in table 4.

Optimization index contrast table after two kinds of optimal way of table 4 are optimized

List of references

Fourth is big, Yuan Jiahai, Hu Zhaoguang. based on user price to should with the Peak-valley TOU power price decision model [J] of satisfaction. Automation of Electric Systems, 2005,29 (20): 10-14.

Tan Zhongfu, Wang Mianbin begs and builds merit etc. Peak-valley TOU power price Optimized model and fuzzy method for solving [J] thereof. and the system engineering theory and practice, 09 phase: 145-151. in 2008

Deb?K,Agrawal?S,Pratap?A,etal.A?fast?elitist?non-dominated?sorting?algnrithm?for?multi-objective?optimization:NSGA-II,Proc?of?the?Prallel?Problem?Soving?from?Nature?VI?Conf,paris,2002:182-197.

It will be appreciated by those skilled in the art that accompanying drawing is the synoptic diagram of a preferred embodiment, the invention described above embodiment sequence number does not represent the quality of embodiment just to description.

The above only is preferred embodiment of the present invention, and is in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, is equal to replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (5)

1. the peak load shifting optimization method of a load curve is characterized in that, said method comprising the steps of:

(1) sets up the peak load shifting Optimized model;

(2) described peak load shifting Optimized model is found the solution, obtain optimum peak load and peak-valley difference;

Described peak load shifting Optimized model is specially:

$\left\{\begin{array}{cc}{Q}_i^{\&prime;}=Q_{i}f\left(P_f\right)& i\&Element;T_f\\ Q_j^{\&prime;}=Q_{j}f\left(P_g\right)& j\&Element;T_g\\ Q_k^{\&prime;}=Q_{k}f\left(P_p\right)+\&lsqb;\underset{i}{\mathrm{\&Sigma;}}(Q_i^{\&prime;}-Q_i)+\underset{j}{\mathrm{\&Sigma;}}(Q_j^{\&prime;}-Q_j)+\underset{k}{\mathrm{\&Sigma;}}(Q_{k}f\left(P_p\right)-Q_k)\&rsqb;/n& k\&Element;T_p\end{array}\right.$

Wherein, P _f, P _p, P _gIt is one group of electricity price; T _f, T _g, T _pExpression peak, paddy, section at ordinary times; Q _i, Q _j, Q _kExpression peak load shifting leading peak, paddy, the power consumption of section at ordinary times;

Expression peak load shifting postpeak, paddy, the power consumption of section at ordinary times; N be at ordinary times the section hourage;

The optimization aim function

(a) peak load value Q _MaxMinimize:

min(Q _max)=min{max[Q(P _f,P _p,P _g,T _f,T _p,T _g)]}

(b) the poor Q of peak load _Max-Q _MinMinimize:

min(Q _max-Q _min)=min{max[Q(P _f,P _p,P _g,T _f,T _p,T _g)]-min[Q(P _f,P _p,P _g,T _f,T _p,T _g)]}

Wherein, Q (P _f, P _p, P _g, T _f, T _p, T _g) for after implementing tou power price, the load value of power consumer after according to customer responsiveness curve peak load shifting;

Constraint condition

(a) implementing tou power price front and back total electricity consumption remains unchanged:

Q=Q _f+Q _p+Q _g=Q _fTOU+Q _pTOU+Q _gTOU

Wherein, Q _f, Q _p, Q _gFor implementing tou power price leading peak, flat, paddy period power consumption; Q _FTOU, Q _PTOU, Q _GTOUFor implementing tou power price postpeak, flat, paddy period power consumption;

(b) electricity price mobility scale constraint:

P _fmin≤P _f≤P _fmax

P _pmin≤P _p≤P _pmax

P _gmin≤P _g≤P _gmax

Wherein, P _Fmin, P _Fmax, P _Pmin, P _Pmax, P _Gmin, P _GmaxConcrete numerical value for peak, flat, paddy day part electricity price change bound;

(c) economic target constraint:

(1-δ)M ₀<Q _fTOUP _f+Q _pTOUP _p+Q _gTOUP _g<M ₀

Wherein, δ=M '/M ₀Be the interest concessions coefficient; M ₀Be the total electricity charge of user before the implementation tou power price; M ' is the power cost that power supply department is saved behind the enforcement tou power price.

2. the peak load shifting optimization method of a kind of load curve according to claim 1 is characterized in that, described described peak load shifting Optimized model is found the solution, and obtains optimum peak load and peak-valley difference and specifically comprises:

(1) actual peak, flat, paddy electricity price are carried out Code And Decode; In the scope of day part electricity price change, produce initial population;

(2) with the objective function of all individual respectively substitution peak loads of current population and peak-valley difference, obtain each individual each corresponding target function value; According to described each target function value described current population is carried out quick non-dominated Sorting, and calculate crowding distance;

(3) calculate virtual fitness by ranking results and described crowding distance, and carry out individual choice, crossover and mutation computing; To application of results elitism strategy after the computing, obtain new progeny population;

(4) take described new progeny population as the basis, repeated execution of steps (2)-(3) until reach maximum iteration time, obtain many groups peak load and peak-valley difference;

(5) from described many groups peak load and peak-valley difference, obtain described optimum peak load and peak-valley difference.

3. the peak load shifting optimization method of a kind of load curve according to claim 2 is characterized in that, described actual peak, flat, paddy electricity price are carried out Code And Decode and be specially:

With the industry average electricity price P before the implementation tou power price ₀Be reference value, to peak, flat, paddy period electricity price P _f, P _p, P _gCarry out the perunit value reduction, coded format is: [x _f, x _p, x _g]; Actual peak, paddy, the ordinary telegram valency is corresponding is decoded as:

$\left\{\begin{array}{c}{P}_f=x_f{P}_0\\ P_p=x_p{P}_0\\ P_g=x_g{P}_0\end{array}\right..$

4. the peak load shifting optimization method of a kind of load curve according to claim 2 is characterized in that, describedly obtains from described many groups peak load and peak-valley difference that load in described optimum peak and peak-valley difference is specially:

From described many groups peak load and peak-valley difference, directly choose described optimum peak load and peak-valley difference according to preset need; Or,

Bring described many groups peak load and peak-valley difference into fuzzy membership function, choose described optimum peak load and peak-valley difference.

5. the peak load shifting optimization method of a kind of load curve according to claim 4 is characterized in that, describedly brings described many groups peak load and peak-valley difference into fuzzy membership function, chooses that load in described optimum peak and peak-valley difference specifically comprises:

Described fuzzy membership function h _iBe specially:

$h_i=\left\{\begin{array}{cc}1& f_i\&le;f_{i\min }\\ \frac{f_{i\max }-f_i}{f_{i\max }-f_{i\min }}& f_{i\min }<f_i<f_{i\max }\\ 0& f_i\&GreaterEqual;f_{i\max }\end{array}\right.$

Wherein, f _iBe peak load or peak-valley difference, i=1,2; f _IminAnd f _ImaxBe respectively maximal value and the minimum value of peak load or peak-valley difference;

Adopt again h=h ₁+ h ₂, corresponding described optimum peak load and peak-valley difference when selecting the h maximum.

Images