CN111598612A - Time-sharing electricity price making method - Google Patents

Time-sharing electricity price making method Download PDF

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CN111598612A
CN111598612A CN202010350680.5A CN202010350680A CN111598612A CN 111598612 A CN111598612 A CN 111598612A CN 202010350680 A CN202010350680 A CN 202010350680A CN 111598612 A CN111598612 A CN 111598612A
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time
wind power
period
peak
valley
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CN111598612B (en
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张刚
解佗
张靠社
罗军刚
闫晔
冯培基
徐奔奔
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Xian University of Technology
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    • G06Q30/0283—Price estimation or determination
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    • G06F18/23—Clustering techniques
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    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention discloses a time-sharing electricity price making method, which comprises the following steps: predicting a wind power interval at a moment to be predicted; randomly sampling in a wind power interval at a moment to be predicted to generate 100 groups of wind power scenes; reducing the scenes into 10 groups of wind power scenes; adding a wind power scene into a user load to generate a net load scene graph; dividing a peak-valley period in a net load scene into H time periods, and then dividing the H time periods into a peak period, a flat period and a valley period; calculating the wind power scene expectation, and generating an equivalent net load curve; constructing a time-of-use electricity price model according to the equivalent net load; and (4) performing multi-objective solution on the time-sharing electricity price model by adopting an NSGA-II algorithm. Compared with a method without considering wind power uncertainty, the method has the advantages that the randomness of the wind power is fully considered, the user is effectively guided to respond to the time-of-use electricity price, the goals of balancing system load and load shifting are achieved, and meanwhile the electricity utilization habits of the user are not influenced.

Description

Time-sharing electricity price making method
Technical Field
The invention belongs to the technical field of new energy, and relates to a time-sharing electricity price making method.
Background
The time-of-use electricity price is a price-based demand response measure, different electricity prices can be set according to different time periods of electricity consumption of a user, the user is guided to reasonably use the electricity, the electricity consumption behavior of the user can be effectively adjusted, and the purposes of peak clipping, valley filling and load curve optimization are achieved.
At present, China has abundant wind power reserves, and governments encourage new energy to be preferentially networked. Wind power can be accessed into a system as a negative load, but the wind power is difficult to predict due to strong fluctuation and poor regularity, so that the wind power has great uncertainty. Therefore, the access of the system can increase the operation pressure of the system, so that the peak-to-valley difference of the original system is increased, and the safe and stable operation of the system is influenced. The time-of-use electricity price research considering the wind power uncertainty is taken as a user side demand response, so that the demand side resource can be fully utilized, the new energy consumption is promoted, the peak-valley difference of the system is effectively adjusted, the economic stable operation capacity of the system is improved, and the time-of-use electricity price research becomes an important research direction at present. Wind power output is regarded as a random variable by a plurality of researchers, an opportunity constraint model and a random fuzzy model are established, and a random probability model describes wind power. The literature assumes that wind speeds obey Rayleigh distribution or Weibull distribution, decomposes a stochastic process of uncertainty into a plurality of typical discrete probability scenes, and calculates the probability of each scene by combining the power characteristics of the wind turbine generator. A scene method is an important method for processing wind power uncertainty, a wind power scene is generated by adopting Monte Carlo sampling, Latin hypercube sampling and other methods after simulated wind power is obtained in most researches, and a backward subtraction method (BR method), a fast forward selection method (FFS method), a scene tree construction method (STC method), a clustering division method and the like are applied to reduce the uncertainty scene to generate a typical wind power scene. The interval prediction is used as a prediction means, the upper limit and the lower limit of the wind power can be accurately predicted, so that more accurate information is provided for scheduling operation, the interval prediction can be used as a method for processing wind power uncertainty in time-of-use electricity price, countless values of the wind power exist in the interval, the wind power is difficult to be butted with the existing load, the formulation of the time-of-use electricity price is influenced, and the uncertainty of the interval prediction needs to be converted into deterministic scene research.
Although the research has a certain discussion on time-of-use electricity price and wind power uncertainty processing, the current method for processing the wind power uncertainty time-of-use electricity price has two problems:
(1) in the existing time-of-use electricity price research of adding wind power, a theoretical probability model is generally used for simulating wind power, and the complexity of a real environment cannot be considered, so that certain error exists between the simulated wind power and the output of a real wind power plant;
(2) the current scene reduction method can conveniently solve the problem of small-scale scene reduction, but the scene scale presents exponential growth along with the increase of time, the accuracy of the scene reduction result is reduced, and the defect that the optimal clustering result cannot be given exists.
The wind power access can greatly relieve the power supply pressure, the time-of-use electricity price can be used as an important demand side response to effectively improve the resource utilization rate, the wind power access and the time-of-use electricity price can be combined to more fully utilize resources, and the resource allocation is optimized. Due to the randomness and the volatility of the wind power, the time-of-use electricity price added with the wind power is difficult to calculate.
Disclosure of Invention
The invention aims to provide a time-sharing electricity price making method, which solves the problem that errors exist between simulated wind power and actual wind power plant output in the prior art.
The technical scheme adopted by the invention is that a time-sharing electricity price making method comprises the following steps:
step 1, predicting a wind power interval at a moment to be predicted by using a continuous wind power sample of a wind power plant;
step 2, randomly sampling in a wind power interval at a moment to be predicted to generate 100 groups of wind power scenes;
step 3, adopting a binary K-means clustering algorithm to reduce the scenes into 10 groups of wind power scenes;
step 4, adding the wind power scene obtained in the step 3 into the user load to generate a net load scene graph;
step 5, dividing a peak-valley period in a net load scene into H time periods by adopting 0-1 integer programming, and then dividing the H time periods into a peak period, a flat period and a valley period;
step 6, calculating the wind power scene expectation, and generating an equivalent net load curve;
step 7, constructing a time-of-use electricity price model according to the equivalent net load, wherein the target function is that the peak-to-valley difference of the equivalent net load of the system is minimum and the electricity utilization dissatisfaction of a user is minimum after the peak-to-valley electricity price is implemented;
and 8, performing multi-target solution on the time-sharing electricity price model by adopting an NSGA-II algorithm to obtain a Pareto optimal solution set, evaluating an objective function and selecting an optimal solution.
The present invention is also characterized in that,
the step 1 specifically comprises the following steps:
step 1.1, setting a condition number tau, a segmentation interval number K and a confidence level beta;
step 1.2, obtaining a discrete probability distribution function according to the continuous wind power sample of the wind power plant and the corresponding edge distribution function
Figure BDA0002471832690000031
Step 1.3, discrete probability distribution function
Figure BDA0002471832690000032
Probability P ofjThe accumulation is carried out from big to small, and when j is accumulated to q,
Figure BDA0002471832690000033
then the edge distribution function at confidence level β lies in the interval
Figure BDA0002471832690000034
In the union of the sub-intervals, the wind power interval at the moment to be predicted is obtained through inverse function calculation
Figure BDA0002471832690000035
And further performing precision evaluation on the wind power interval to be predicted at the moment by adopting PIAW and PICP indexes.
The step 3 specifically comprises the following steps:
step 3.1, firstly, forming 100 groups of wind power scenes into clusters;
3.2, selecting a cluster from the cluster table;
3.3, dividing the cluster into two clusters by adopting a K-means algorithm;
step 3.4, repeating the step 3.3 until the preset experiment times are reached;
3.5, selecting two clusters with the minimum total error from the clusters obtained in the step 3.4;
step 3.6, adding the two clusters obtained in the step 3.5 into a cluster table;
and 3.7, repeating the steps 3.2-3.6 until the cluster table contains 10 clusters.
The step 5 specifically comprises the following steps:
step 5.1, dividing the peak-valley period in the net load scene into H time periods, assuming that i is the starting time of a certain divided period, j is the ending time, and defining the distance between the objects i and j as dijThen matrix [ dij]Is an N × N matrix, and the row and column are marked as 0,1, … N-1;
the objective function of the model divided into H clustering periods during the peak-valley period is
Figure BDA0002471832690000041
In the above formula, zijIs a variable from 0 to 1, and when the starting time of a certain period is i and the ending time is j, z isijThe value is 1, otherwise 0; dijFor all d in the same time periodijThe sum of (1):
Figure BDA0002471832690000042
in the above formula, mod is a modulo operation;
the constraint conditions of the model for dividing the peak-valley period into H clustering periods are as follows:
Figure BDA0002471832690000043
Figure BDA0002471832690000044
and 5.2, dividing the H time periods into peak time periods, flat time periods and valley time periods.
The step 6 specifically comprises the following steps:
the wind power scene obtained in step 6.1 and step 3 is expected to be:
Figure BDA0002471832690000051
in the above equation, theta represents a certain possible wind power scenario,
Figure BDA0002471832690000052
for each scene probability value, PwindFor wind power output power, thetawIs a wind power scene set;
step 6.2, executing an equivalent net load expression before peak-valley electricity price as follows:
Figure BDA0002471832690000053
in the above equation, q (t) is the user load.
The step 7 specifically comprises the following steps:
step 7.1, constructing an objective function according to the equivalent net load:
the target function 1, the minimum equivalent net load difference of the system in the peak and valley period after the peak-valley electricity price is implemented:
minC＝min[maxL(t)-minL(t)](16)；
in the above formula, l (t) is the system equivalent payload after peak-to-valley electricity prices are implemented;
objective function 2, minimum user electricity dissatisfaction:
Figure BDA0002471832690000054
in the above formula, the first and second carbon atoms are,
Figure BDA0002471832690000055
representing the change rate of the electricity consumption of the user in the t period;
and 7.2, calculating the load transfer rate, and fitting according to historical data to obtain a user response curve:
Figure BDA0002471832690000056
in the above formula,. DELTA.pabIs the difference in electricity prices of the time period a and the time period b, habSaturation region threshold for electricity price difference,/abDead zone threshold for electrovalence difference, KabIs the slope of the linear region, λ, during load transfermaxThe maximum load transfer rate;
the load transfer rate lambda from the peak period of the equivalent net load to the ordinary period can be obtained by the same methodfpLoad transfer rate lambda from peak period to valley period of equivalent net loadfgLoad transfer rate lambda from flat period to valley period of equivalent net loadpgThe load of each period after the time-of-use electricity price is executed is thus obtained as:
Figure BDA0002471832690000061
in the above formula, Tf、Tp、TgRespectively represent three periods of peak-to-valley, Lk0,LkLoads before and after the time-of-use electricity price are executed;
Figure BDA0002471832690000062
the equivalent net load average value of each time interval before the time-of-use electricity price is executed;
and 7.3, constructing constraint conditions:
constraint 1, time constraint:
T＝Tf+Tp+Tg＝24 (20)；
constraint 2, load constraint:
L＝Lf+Lp+Lg(21)；
constraint condition 3, the expenditure of the user electricity rate after the implementation of the time-of-use electricity rate is less than or equal to the expenditure cost before the implementation of the time-of-use electricity rate:
Figure BDA0002471832690000063
Figure BDA0002471832690000064
U0≥UTOU(24)；
constraint condition 4, limiting the peak-to-valley electrovalence ratio after implementation of time-of-use electrovalence:
Figure BDA0002471832690000065
in the above formula, k1And k2Is a constant that limits the peak-to-valley electrovalence ratio.
The invention has the beneficial effects that:
the invention relates to a time-sharing electricity price making method, which comprises the steps of establishing a discrete probability distribution function at a time to be predicted, mining a correlation relation between wind power sequences of adjacent time periods to predict a wind power interval, improving the wind power prediction precision and reducing the difference between the wind power and the actual wind power; by combining the prediction interval with random sampling, the uncertainty of interval prediction is converted into a deterministic scene, and binary K-means is used for reducing the scene, so that the accuracy is high, the efficiency is high, a complex substitute model is not required to be searched, the calculation efficiency can be improved, and the problem of combining the interval prediction result with time-of-use electricity price customization is solved; under the condition of considering multiple scenes, the peak-valley period is divided by adopting an integer programming method, so that the problem of continuity of the period can be effectively solved; establishing a multi-objective optimization time-of-use electricity price model based on the psychological response of consumers according to the minimum peak-valley difference and the minimum user satisfaction, and optimizing model parameters by adopting an NSGA-II multi-objective method to obtain a non-inferior solution set of the time-of-use electricity price model; compared with a method without considering wind power uncertainty, the method has the advantages that the randomness of the wind power is fully considered, the user is effectively guided to respond to the time-of-use electricity price, the goals of balancing system load and load shifting are achieved, and meanwhile the electricity utilization habits of the user are not influenced.
Drawings
FIG. 1 is a flow chart of a time-of-use electricity pricing method of the present invention;
FIG. 2 is a load transfer rate graph of a time-of-use pricing method of the present invention;
fig. 3 is a flow chart of the NSGA-II algorithm employed by the time-of-use electricity price making method of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
A time-sharing electricity price making method, as shown in fig. 1, comprising the steps of:
step 1, predicting a wind power interval at a moment to be predicted by using a continuous wind power sample of a wind power plant;
step 1.1, setting a condition number tau, a segmentation interval number K and a confidence level beta;
step 1.2, obtaining a discrete probability distribution function according to the continuous wind power sample of the wind power plant and the corresponding edge distribution function
Figure BDA0002471832690000071
Step 1.2.1, suppose the wind power sequence at the continuous t +1 moment is [ X ]T-t,XT-t-1,…,XT]：
Figure BDA0002471832690000081
In the above formula, each row represents a sample in the X domain;
calculating an edge distribution function value corresponding to the wind power historical sample as follows:
Figure BDA0002471832690000082
in the above formula, FT-t,…FTWind power edge distribution functions in the T-T and … T periods respectively, wherein each row represents a sample in the F domain and has N samples;
step 1.2.2, assume FT-t,…FTThe belonging interval (0, 1) includes K intervals, which are respectively the interval S1,…SKIn which S is1＝[0,]，＝1/K，Sj＝[(j-1),j]J is 2, … K, K is a positive integer, then the wind power sequence [ XT-t,XT-t-1,…,XT]Composition Kt+1Subspace, then any one sample in the F domain
Figure BDA0002471832690000083
Necessarily falling into the subspace of the wind power sequence
Figure BDA0002471832690000084
Wherein, P1,…,Pl…Pt∈ {1,2, …, K }; i.e., F;)T-t(xT-t-1)∈((P1-1),P1],…,FT(xT)∈((Pt+1-1),Pt+1]；
Selecting N in formula (2)1The samples form a condition matrix, and the first t elements of each sample are respectively connected with
Figure BDA0002471832690000085
Falling within the same interval, the condition matrix is:
Figure BDA0002471832690000086
step 1.2.3, for N in formula (3)1Dividing each sample into J types, classifying the samples of which all elements in the t +1 th column fall in the same subinterval into the same type, wherein the number of the samples of each type is as follows: m1,…Mj,…,MJ；
Step 1.2.4, the zone where the t +1 th element of each type of sample is locatedThe same in each case, but different in value; in order to obtain a same value representing the t +1 th element in each type of sample, the value of the t +1 th column of each type of sample is calculated by using formula (4)
Figure BDA0002471832690000091
Probability of occurrence P of each type of samplej：
Figure BDA0002471832690000092
If it will be
Figure BDA0002471832690000093
As a scene, PjAs is the probability of the scene occurring,
Figure BDA0002471832690000094
to be composed of
Figure BDA0002471832690000095
Is a condition of
Figure BDA0002471832690000096
A discrete probability distribution function of (2).
Step 1.3, the discrete probability distribution function
Figure BDA0002471832690000097
Probability P ofjThe accumulation is carried out from big to small, and when j is accumulated to q,
Figure BDA0002471832690000098
then the edge distribution function at confidence level β lies in the interval
Figure BDA0002471832690000099
In the union of the sub-intervals, the wind power interval at the moment to be predicted is obtained through inverse function calculation
Figure BDA00024718326900000910
In particular toOf discrete probability distribution functions
Figure BDA00024718326900000911
The data are sorted in descending order according to the probability, and the discrete probability distribution function after the sorting is recorded as
Figure BDA00024718326900000912
P(1)To maximum probability, P(J)Is the minimum probability; starting from j to 1 for P(1)Accumulation is stopped until the accumulated sum is β or more, and it is assumed that when j is accumulated q,
Figure BDA00024718326900000913
then the edge distribution function at confidence level β lies in the interval
Figure BDA00024718326900000914
In the union of the sub-intervals, the following assumptions are made:
Figure BDA00024718326900000915
the interval of the edge distribution function is then:
Figure BDA00024718326900000916
by means of an inverse function
Figure BDA00024718326900000917
Calculating the interval of the wind power occurring at the Tth moment under the confidence level β
Figure BDA00024718326900000918
And obtaining the wind power interval at the moment to be predicted.
And performing precision evaluation on discrete probability distribution function prediction results under the values of m and K by using two indexes, namely PIAW and PICP.
Specifically, the prediction accuracy of the proposed algorithm is verified by using a section coverage (PICP) and a section average width (PIAW) as evaluation criteria, and the calculation formula of the section coverage (PICP) is as follows:
Figure BDA0002471832690000101
in the above formula, U is the total number of the wind power to be predicted, and U is 1,2, … and U; au is a linear function defined as:
Figure BDA0002471832690000102
when the actual value of the wind power at the moment to be predicted falls into the prediction interval, the value of Au is 1, otherwise, the value of Au is 0; in the formula
Figure BDA0002471832690000103
And uVrespectively the upper and lower boundaries of the prediction interval, VuThe actual value of the wind power at the predicted moment is obtained.
The interval average width (PIAW) is calculated as:
Figure BDA0002471832690000104
the PICP represents the number of the actual values of the wind power in the prediction interval, and on the basis of meeting the confidence level, the larger the value of the PICP is, the more the number of the actual values of the wind power in the prediction interval is, which means the better the prediction effect is. The PIAW represents the average width of the upper and lower boundaries of the interval, and when the same confidence level is satisfied, the smaller the value, the narrower the prediction interval is, meaning that the prediction interval is closer to the value that actually occurs.
Step 2, randomly sampling in a wind power interval at a moment to be predicted to generate 100 groups of wind power scenes;
step 3, adopting a binary K-means clustering algorithm to reduce the scenes into 10 groups of wind power scenes;
step 3.1, after initializing 100 groups of wind power scenes, grouping the 100 groups of wind power scenes into clusters;
3.2, selecting a cluster from the cluster table;
3.3, dividing the cluster into two clusters by adopting a K-means algorithm;
step 3.4, repeating the step 3.3 until the preset experiment times are reached;
3.5, selecting two clusters with the minimum total error from the clusters obtained in the step 3.4;
step 3.6, adding the two clusters obtained in the step 3.5 into a cluster table;
and 3.7, repeating the steps 3.2-3.6 until the cluster table contains 10 clusters.
Step 4, adding the wind power scene obtained in the step 3 into the user load to generate a net load scene graph;
step 5, dividing the time interval of the net load scene;
firstly, dividing a net load scene into H time periods by adopting 0-1 integer programming, wherein the total time number of the peak-valley time periods is T, and H can be set to be any integer of 1-24 hours according to the load condition of a user; and dividing the H time periods into peak, flat and valley time periods according to the load.
And 5.1, dividing a net load scene into H time periods by adopting 0-1 integer programming, and assuming that i is the starting time of a certain divided time period and j is the ending time. The problem of the continuity of the time intervals needs to be considered in the time interval division, if the starting time i of a certain divided time interval is smaller than the ending time j, any time r (i < r < j) belongs to the same time interval; if the starting time i is greater than the ending time j, the time interval ranges from i to r to T and from 1 to j.
In this embodiment, the object to be analyzed is T moments to be divided, and the parameter at a certain moment is a payload numerical vector at the moment in different scenes, and the payload numerical vector should be normalized in practical application. Defining the distance between the instants i, j as dijThen matrix [ dij]Is an N × N matrix, and the row and column are marked as 0,1, … N-1;
the objective function of the model divided into H clustering periods during the peak-valley period is
Figure BDA0002471832690000121
In the above formula, zijIs a variable from 0 to 1, and when the starting time of a certain period is i and the ending time is j, z isijThe value is 1, otherwise 0; dijFor all d in the same time periodijThe sum of (1):
Figure BDA0002471832690000122
in the above formula, mod is a modulo operation;
the constraint conditions of the model for dividing the peak-valley period into H clustering periods are as follows:
Figure BDA0002471832690000123
Figure BDA0002471832690000124
wherein the constraint equation (12) indicates that the time w can only be contained in a single time period, and the left side of the time w is the sum of all the divisions which may contain the time w. In the period containing w, the following 3 cases can be classified: i is more than or equal to 0 and less than or equal to j and less than or equal to N-1, i is more than or equal to 0 and less than or equal to i and less than or equal to N-1, and w is more than or equal to 0 and less than or equal to j and less than or equal to i and less than or equal to N-1; in the constraint condition formula (13), the total number of the divided time periods is H;
and 5.2, dividing the H time periods into peak time periods, flat time periods and valley time periods.
Step 6, calculating the wind power scene expectation, and generating an equivalent net load curve;
the wind power scenario obtained by step 3 is expected to be:
Figure BDA0002471832690000125
in the above equation, theta represents a certain possible wind power scenario,
Figure BDA0002471832690000126
for each scene probability value, PwindFor wind power output power, thetawIs a wind power scene set;
the system equivalent net load expression before the peak-valley electricity price is implemented is as follows:
Figure BDA0002471832690000127
in the above equation, q (t) is the user load.
Step 7, constructing a time-of-use electricity price model according to the equivalent net load, wherein the target function is that the peak-to-valley difference of the equivalent net load of the system is minimum and the electricity utilization dissatisfaction of a user is minimum after the peak-to-valley electricity price is implemented;
step 7.1, constructing an objective function:
the target function 1, the minimum equivalent net load difference of the system in the peak and valley period after the peak-valley electricity price is implemented:
minC＝min[maxL(t)-minL(t)](16)；
in the above formula, l (t) is the system equivalent net load after the peak-to-valley electricity price is implemented, and C represents the difference between the peak and the valley;
objective function 2, minimum user electricity dissatisfaction:
Figure BDA0002471832690000131
in the above formula, the first and second carbon atoms are,
Figure BDA0002471832690000132
representing the change rate of the electricity consumption of the user in the t period;
and 7.2, considering the responsiveness of the user in the solving process of the objective function, and considering that the user has different degrees of responses to different electricity prices according to the psychology theory of the consumer, so that different consumption behaviors can be caused. The price of electricity is a stimulus to the user for which the response is not infinite, but rather there is a threshold of difference. When the stimulation is too large to exceed the upper threshold, the user reaches a saturated state and basically does not transfer the load any more, and the power price range which is larger than the upper threshold is called a response saturation region; similarly, when the stimulation is too small to be lower than the lower threshold, the user will not feel as the response is not made, and the range of the electricity prices lower than the lower threshold is called a dead zone. Within the threshold range, the user will respond to the stimulus with a degree of response approximately linear to the magnitude of the stimulus, referred to as the linear region, so that an approximate piecewise function is obtained, which is determined by three parameters, respectively: a dead zone threshold, a linear segment slope, and a saturation zone threshold. Different types of user responses will be embodied with different parameters.
The concept of load transfer rate is introduced here: the load transfer rate is defined as the ratio of the load transfer amount of the user load from the high electricity rate period to the low electricity rate period to the load at the high electricity rate period after the peak-to-valley electricity rate is applied. The load transfer rate is assumed to be proportional to the difference in electricity prices between the peak level, peak valley, and valley level. Calculating the electricity price difference and the load transfer rate of different periods of the peak, the plateau and the valley based on a large amount of historical data, and fitting a piecewise function curve approximating to the actual response curve of the user, as shown in fig. 2, wherein the abscissa represents the electricity price difference of different periods, and the ordinate represents the load transfer rate:
Figure BDA0002471832690000141
in the above formula,. DELTA.pabIs the difference in electricity prices of the time period a and the time period b, habSaturation region threshold for electricity price difference,/abDead zone threshold for electrovalence difference, KabIs the slope of the linear region, λ, during load transfermaxThe maximum load transfer rate;
the load transfer rate lambda from the peak period of the equivalent net load to the ordinary period can be obtained by the same methodfpLoad transfer rate lambda from peak period to valley period of equivalent net loadfgLoad transfer rate lambda from flat period to valley period of equivalent net loadpgAnd thus the load of each time period after the time-of-use electricity price is executed is:
Figure BDA0002471832690000142
in the above formula, Tf、Tp、TgRespectively generation by generationThree periods of table peak valley, Lk0,LkLoads before and after the time-of-use electricity price are executed;
Figure BDA0002471832690000143
is an equivalent net load average value of each time interval before the time-of-use electricity price is executed.
And 7.3, constructing constraint conditions:
constraint 1, time constraint:
T＝Tf+Tp+Tg＝24 (20)
constraint 2, electric quantity (load) constraint:
L＝Lf+Lp+Lg(21)
constraint 3, in order to make the user actively shift the load during peak hours to the low-peak hours, the expenditure of the user electricity rate after the time-of-use electricity rate is equal to or less than the expenditure cost before the time-of-use electricity rate:
Figure BDA0002471832690000151
Figure BDA0002471832690000152
U0≥UTOU(24)；
constraint condition 4, in order to avoid that the peak-valley electricity price difference is too large after the time-of-use electricity price is implemented, excessive user response occurs, and the peak and valley are inverted; or the peak-to-valley electricity price is not obvious, the user response is insufficient, and the expected target cannot be reached, so the range of the peak-to-valley electricity price ratio is limited:
Figure BDA0002471832690000153
in the above formula, k1And k2The peak-to-valley electrovalence ratio is limited, and in China, the value of the peak-to-valley electrovalence ratio is generally between 2 and 5.
And 8, performing multi-target solution on the time-sharing electricity price model by adopting an NSGA-II algorithm, obtaining a Pareto optimal solution set as shown in FIG. 3, evaluating a target function, and selecting an optimal solution.
Step 8.1: reading in equivalent net load data, and performing algorithm parameter setting and variable range setting, wherein the setting parameters comprise an optimal individual coefficient, a population size, a maximum evolution algebra and a fitness function deviation; the variable range refers to decision variables (allowable range of peak, flat and valley electricity prices);
step 8.2: randomly generating an initial population P0And performing non-dominant sorting;
step 8.3: obtaining a first generation offspring population through three basic operations of selection, crossing and mutation after non-dominant sorting;
step 8.3: merging the parent population and the child population, performing rapid non-dominant sorting, and simultaneously performing crowding degree calculation on the individuals in each non-dominant layer;
step 8.4: selecting proper individuals to form a new parent population according to the non-dominant relationship and the crowding degree of the individuals;
step 8.5: and (4) obtaining a next generation filial population through three basic operations of selection, crossing and mutation, and executing the step 8.3 until the maximum evolution generation number is reached.
Step 8.6: after iteration is finished, a Pareto optimal solution set is obtained, and an optimal solution is selected according to the following method;
firstly, carrying out standardization processing on peak-valley difference and user dissatisfaction data in a Pareto optimal solution set by utilizing an equation (26); in the formula xiFor values to be normalized, xjFor normalized value, MIN ═ MIN (x)i)。
Figure BDA0002471832690000161
After data standardization is carried out, setting the weight of the peak-valley difference and the user dissatisfaction degree according to the importance degrees of two precision indexes of the peak-valley difference and the user dissatisfaction degree; and finally, performing weighted calculation on the two evaluation indexes of all points in the Pareto optimal solution, wherein the point with the minimum weighted value is the optimal solution.
Through the mode, the time-sharing electricity price making method establishes the discrete probability distribution function of the time to be predicted, and mines the correlation among the wind power sequences of adjacent time periods to predict the wind power interval, so that the wind power prediction precision can be improved, and the difference between the wind power prediction precision and the actual wind power is reduced; by combining the prediction interval with random sampling, the uncertainty of interval prediction is converted into a deterministic scene, and binary K-means is used for reducing the scene, so that the accuracy is high, the efficiency is high, a complex substitute model is not required to be searched, the calculation efficiency can be improved, and the problem of combining the interval prediction result with time-of-use electricity price customization is solved; under the condition of considering multiple scenes, the peak-valley period is divided by adopting an integer programming method, so that the problem of continuity of the period can be effectively solved; establishing a multi-objective optimization time-of-use electricity price model based on the psychological response of consumers according to the minimum peak-valley difference and the minimum user satisfaction, and optimizing model parameters by adopting an NSGA-II multi-objective method to obtain a non-inferior solution set of the time-of-use electricity price model; compared with a method without considering wind power uncertainty, the method has the advantages that the randomness of the wind power is fully considered, the user is effectively guided to respond to the time-of-use electricity price, the goals of balancing system load and load shifting are achieved, and meanwhile the electricity utilization habits of the user are not influenced.

Claims (7)

1. A time-sharing electricity price making method is characterized by comprising the following steps:
step 1, predicting a wind power interval at a moment to be predicted by using a continuous wind power sample of a wind power plant;
step 2, randomly sampling and generating 100 groups of wind power scenes in the wind power interval at the moment to be predicted;
step 3, adopting a binary K-means clustering algorithm to reduce the scenes into 10 groups of wind power scenes;
step 4, adding the wind power scene obtained in the step 3 into the user load to generate a net load scene graph;
step 5, dividing a peak-valley period in a net load scene into H time periods by adopting 0-1 integer programming, and then dividing the H time periods into a peak period, a flat period and a valley period;
step 6, calculating the wind power scene expectation, and generating an equivalent net load curve;
step 7, constructing a time-of-use electricity price model according to the equivalent net load, wherein the target function is that the peak-to-valley difference of the equivalent net load of the system is minimum and the electricity utilization dissatisfaction of a user is minimum after the peak-to-valley electricity price is implemented;
and 8, performing multi-target solution on the time-of-use electricity price model by adopting an NSGA-II algorithm to obtain a Pareto optimal solution set, evaluating a target function and selecting an optimal solution.
2. The time-sharing electricity price making method according to claim 1, wherein the step 1 specifically comprises:
step 1.1, setting a condition number tau, a segmentation interval number K and a confidence level beta;
step 1.2, obtaining a discrete probability distribution function according to the continuous wind power sample of the wind power plant and the corresponding edge distribution function
Figure FDA0002471832680000011
Step 1.3, the discrete probability distribution function
Figure FDA0002471832680000012
Probability P ofjThe accumulation is carried out from big to small, and when j is accumulated to q,
Figure FDA0002471832680000013
then the edge distribution function at confidence level β lies in the interval
Figure FDA0002471832680000014
In the union of the sub-intervals, the wind power interval at the moment to be predicted is obtained through inverse function calculation
Figure FDA0002471832680000021
3. The time-sharing electricity price making method according to claim 1 or 2, characterized by further comprising the step of performing precision evaluation on the wind power interval at the time to be predicted by using PIAW and PICP indexes.
4. The method for formulating a time-of-use electricity price according to claim 1, wherein the step 3 specifically comprises:
step 3.1, firstly, forming 100 groups of wind power scenes into clusters;
3.2, selecting the cluster from a cluster table;
3.3, dividing the cluster into two clusters by adopting a K-means algorithm;
step 3.4, repeating the step 3.3 until the preset experiment times are reached;
3.5, selecting two clusters with the minimum total error from the clusters obtained in the step 3.4;
step 3.6, adding the two clusters obtained in the step 3.5 into a cluster table;
and 3.7, repeating the steps 3.2-3.6 until the cluster table contains 10 clusters.
5. The time-sharing electricity price making method according to claim 1, wherein the step 5 specifically comprises:
step 5.1, dividing the peak-valley period in the net load scene into H time periods, assuming that i is the starting time of a certain divided period, j is the ending time, and defining the distance between the objects i and j as dijThen matrix [ dij]Is an N × N matrix, and the row and column are marked as 0,1, … N-1;
the objective function of the model divided into H clustering periods in the peak-valley period is
Figure FDA0002471832680000022
In the above formula, zijIs a variable from 0 to 1, and when the starting time of a certain period is i and the ending time is j, z isijThe value is 1, otherwise 0; dijFor all d in the same time periodijThe sum of (1):
Figure FDA0002471832680000031
in the above formula, mod is a modulo operation;
the constraint conditions of the model for dividing the peak-valley period into H clustering periods are as follows:
Figure FDA0002471832680000032
Figure FDA0002471832680000033
and 5.2, dividing the H time periods into peak time periods, flat time periods and valley time periods.
6. The method for formulating a time-of-use electricity price according to claim 1, wherein the step 6 specifically comprises:
the wind power scene obtained in step 6.1 and step 3 is expected to be:
Figure FDA0002471832680000034
in the above equation, theta represents a certain possible wind power scenario,
Figure FDA0002471832680000035
for each scene probability value, PwindFor wind power output power, thetawIs a wind power scene set;
step 6.2, executing an equivalent net load expression before peak-valley electricity price as follows:
Figure FDA0002471832680000036
in the above equation, q (t) is the user load.
7. The method for formulating a time-of-use electricity price according to claim 1, wherein the step 7 specifically includes:
step 7.1, constructing an objective function according to the equivalent net load:
the target function 1, the minimum equivalent net load difference of the system in the peak and valley period after the peak-valley electricity price is implemented:
min C＝min[max L(t)-min L(t)](16)；
in the above formula, l (t) is the system equivalent payload after peak-to-valley electricity prices are implemented;
objective function 2, minimum user electricity dissatisfaction:
Figure FDA0002471832680000041
in the above formula, the first and second carbon atoms are,
Figure FDA0002471832680000042
representing the change rate of the electricity consumption of the user in the t period;
and 7.2, calculating the load transfer rate, and fitting according to historical data to obtain a user response curve:
Figure FDA0002471832680000043
in the above formula,. DELTA.pabIs the difference in electricity prices of the time period a and the time period b, habSaturation region threshold for electricity price difference,/abDead zone threshold for electrovalence difference, KabIs the slope of the linear region, λ, during load transfermaxThe maximum load transfer rate;
the load transfer rate lambda from the peak period of the equivalent net load to the ordinary period can be obtained by the same methodfpLoad transfer rate lambda from peak period to valley period of equivalent net loadfgLoad transfer rate lambda from flat period to valley period of equivalent net loadpgThe load of each period after the time-of-use electricity price is executed is thus obtained as:
Figure FDA0002471832680000044
in the above formula, Tf、Tp、TgRespectively represent three periods of peak-to-valley, Lk0,LkLoads before and after the time-of-use electricity price are executed;
Figure FDA0002471832680000045
the equivalent net load average value of each time interval before the time-of-use electricity price is executed;
and 7.3, constructing constraint conditions:
constraint 1, time constraint:
T＝Tf+Tp+Tg＝24 (20)；
constraint 2, load constraint:
L＝Lf+Lp+Lg(21)；
constraint condition 3, the expenditure of the user electricity rate after the implementation of the time-of-use electricity rate is less than or equal to the expenditure cost before the implementation of the time-of-use electricity rate:
Figure FDA0002471832680000051
Figure FDA0002471832680000052
U0≥UTOU(24)；
constraint condition 4, limiting the peak-to-valley electrovalence ratio after implementation of time-of-use electrovalence:
Figure FDA0002471832680000053
in the above formula, k1And k2Is a constant that limits the peak-to-valley electrovalence ratio.
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