CN112053059A - Peak-valley load optimization method based on user demand elastic response and time-of-use electricity price - Google Patents
Peak-valley load optimization method based on user demand elastic response and time-of-use electricity price Download PDFInfo
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Abstract
The invention relates to a peak-valley load optimization method based on user demand elastic response and time-of-use electricity price, which comprises the following steps: acquiring load and real-time electricity price data of a power system; determining the peak-valley membership degree of the load, carrying out peak-valley average time interval division on the load to obtain a user elastic response matrix, and establishing a demand response model of the user load; constructing a peak-valley time-of-use electricity price and load optimization model taking the minimum system peak-valley difference as a target function, wherein the constraint conditions are respectively constant total demand of electricity load, peak-valley electricity price constraint and income constraint of a user and a power supply company; and optimizing peak-valley load by adopting a genetic algorithm. According to the invention, peak-valley load can be optimized based on user demand elastic response and time-of-use electricity price, and the optimized time-of-use electricity price can fully guide the electricity utilization behavior of a user, so that the distribution of user load is optimized, and the problem of overlarge load peak-valley difference can be solved.
Description
Technical Field
The invention belongs to the technical field of automatic control of power systems, and particularly relates to a peak-valley load optimization method based on user demand elastic response and time-of-use electricity price.
Background
Along with the rapid development of economy and the improvement of living standard of people in China, the electrification degree is higher and higher, and the increase range of electric power demand is larger and larger. The increasing power demand makes the problem of the shortage of power resources in China more severe, and in addition, the differentiation of the electricity consumption habits of residents and the large-scale grid connection of clean energy lacking stability are realized, the periodic characteristics of power load fluctuation are obvious, the peak-valley difference of the load is increased, and great challenges are brought to the safe and stable operation of a power grid. By flexibly responding to the demand side resources and perfecting the time-of-use electricity price system, the household energy behavior is optimized, the load peak-valley difference is reduced, the situation of short power supply is relieved, the safe and stable operation of a power grid is guaranteed, and the social benefit efficiency is improved.
With the development of the current smart power grid and smart monitoring equipment, a user is more sensitive to power price change, and the response to time-sharing power price is more obvious. Therefore, a reasonable time-of-use electricity price model is needed to be established, the advantages of the time-of-use electricity price are fully exerted, the users are guided to use electricity scientifically and reasonably, the utilization rate of power resources is improved, and the configuration of the power resources is optimized.
Therefore, based on the problems, the peak-valley load optimization method based on the user demand elastic response and the time-of-use electricity price is provided for solving the problems of unbalanced load and overlarge load peak-valley difference of the power system, and has important practical significance.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a peak-valley load optimization method based on user demand elastic response and time-of-use electricity price, aiming at the problems of unbalanced load and overlarge load peak-valley difference of a power system.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
the peak-valley load optimization method based on the user demand elastic response and the time-of-use electricity price comprises the following steps of:
acquiring load and real-time electricity price data of a power system;
determining the peak-valley membership degree of the load, carrying out peak-valley average time interval division on the load to obtain a user elastic response matrix, and establishing a demand response model of the user load;
constructing a peak-valley time-of-use electricity price and load optimization model taking the minimum system peak-valley difference as a target function, wherein the constraint conditions are respectively constant total demand of electricity load, peak-valley electricity price constraint and income constraint of a user and a power supply company;
and (4) obtaining the optimized time-of-use electricity price by adopting a genetic algorithm, and optimizing peak-valley load.
Further, determining the peak-valley membership of the load, dividing the peak-valley average time period of the load to obtain a user elastic response matrix, and establishing a demand response model of the user load, wherein the steps comprise:
(1) determining the maximum load, the minimum load, the peak-valley difference, the load rate and the peak-valley difference rate according to the obtained load data:
a＝minai
b＝maxai
d＝b-a
wherein a and b are respectively the minimum and maximum load, aiLoad at time i, d is the peak-to-valley difference, r1Is the load factor, r2The peak-to-valley difference rate;
(2) determining the peak-valley membership degree of the load, and carrying out standardization:
the membership degree of peak and valley time sections is as follows:
in the formula, xi，1Degree of membership, x, of the load with respect to the peak segment at time ii，2The membership degree of the load related to the valley section at the ith moment;
and (3) carrying out standardization treatment:
in formula (II), x'i,kTaking 1 and 2 as k to respectively represent peak section and valley section for the normalized membership degree of the peak section and the valley section, and xavg,kAverage value of degree of membership of load to crest or trough at all times of day, skThe standard deviation of the load on the membership degree of the peak section or the valley section at all the moments in one day;
(3) dividing peak-valley leveling time periods according to the peak-valley membership;
(4) acquiring the electric power demand elasticity coefficient and the load change rate of a user:
elastic coefficient of power demand:
in the formula, i and j take 1, 2 and 3 to respectively represent three periods of peak-to-valley level,ijfor the elastic coefficient of the impact of the change in electricity price corresponding to the period j on the load demand of the period i, qi、ΔqiLoad demand and load variation, p, respectively, for period ij、ΔpjElectricity price and electricity price variation, Q, respectively, corresponding to the j time periodiThe electric load quantity, Q, corresponding to the i time period after the time-of-use electricity price is implementedi0The load amount of the corresponding time interval before the time-of-use electricity price is implemented;
load change rate of the user in the i period:
λii＝ii
in the formula, Pi0For applying electricity prices at corresponding time periods before time-of-use electricity prices, PiFor applying the electricity prices at the corresponding time intervals after the time-of-use electricity prices, KiTo implement the rate of change of electricity prices, λ, of the user during the period i after the time of use of electricity pricesiRepresents the user load change rate, lambda, before and after the user carries out the time-of-use electricity priceiiFor the rate of reduction/increase of the stiffness load of the user, lambdaij(i ≠ j) is the non-rigid load transfer rate of the user;
(5) determining a load demand response model based on a user demand elasticity matrix:
the user elastic response matrix is:
electric load demand response model:
in the formula, Q10、Q20、Q30Respectively the load of the user in each time interval at peak-valley level before the time-of-use electricity price is implemented, lambdaijAs rate of change of user load, T1、T2、T3Representing three periods of peak-to-valley, respectively.
Further, a peak-valley time-of-use electricity price and load optimization model taking the minimum system peak-valley difference as a target function is constructed:
F＝min(maxQi-minQi),(i＝1,2,...,24)
in the formula, maxQiAt the maximum value of the system load, minQiIs the minimum value of the system load;
the constraint conditions are respectively the constant electricity load, the constraint of electricity price, the benefit of users and the benefit of power supply companies:
and (3) power load invariant constraint:
Q1+Q2+Q3＝Q10+Q20+Q30
in the formula, Q1、Q2、Q3Respectively the peak, valley and ordinary period load after the time-of-use electricity price is implemented, Q10、Q20、Q30Respectively the peak, valley and ordinary time load of the user before the time-of-use electricity price is implemented;
and (4) electricity price constraint:
in the formula, P1、P2、P3Electricity prices, P, in peak, valley, flat periods, respectively1min、P2min、P3min、P1max、P2max、P3maxThe lower limit and the upper limit of the price change in three periods of time respectively, x is the price change proportion, k is the price ratio change range, m1、m2Lower limit and upper limit of peak-to-valley period, P0The electricity price before the implementation of the time-of-use electricity price;
user and power company benefit constraints:
M0＝P0(Q10+Q20+Q30)
M1＝P1Q1+P2Q2+P3Q3
M1≤M0
M1≥M0-Mk
in the formula, M0、M1The electric charge expenditure of the user before and after implementing the time-of-use electricity price and the electricity selling income of the power supply company are respectively, and M' is the capacity investment saved due to the reduction of the load of implementing the time-of-use electricity price.
Further, solving a peak-valley time-of-use electricity price and load optimization model which takes the minimum system peak-valley difference as an objective function by adopting a genetic algorithm to obtain the optimized time-of-use electricity price and optimize the peak-valley load.
Further, according to the membership degree of the peak and the valley, the method for dividing the peak-valley level time period comprises the following steps:
determining a threshold value theta1,θ2Comparing the peak-to-valley membership to an initial threshold value when x'i,1＜θ1When i belongs to the valley period, when θ1≤x'i,1＜θ2When i belongs to the period of flatness, when x'i,1≥θ2When i belongs to a peak period;
wherein the threshold value theta1,θ2Determining according to the evaluation index S of the peak-valley average division effect:
wherein S is an index for evaluating the effect of peak-to-valley leveling, Pmax,Pmin,Vmax,Vmin,Mmax,MminMaximum and minimum load values within the peak, valley, plateau interval, respectively, are determined by:
maxS(f(θ1,θ2))
s.t(θ1,θ2)∈[0,1]
solve for the threshold value theta1,θ2And obtaining peak-to-valley average time interval division.
The invention has the advantages and positive effects that:
the method can optimize the peak-valley load based on the user demand elastic response and the time-of-use electricity price, and the optimized time-of-use electricity price can fully guide the electricity utilization behavior of the user, so that the distribution of the user load is optimized, the problem of overlarge load peak-valley difference can be solved.
Drawings
The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and examples, but it should be understood that these drawings are designed for illustrative purposes only and thus do not limit the scope of the present invention. Furthermore, unless otherwise indicated, the drawings are intended to be illustrative of the structural configurations described herein and are not necessarily drawn to scale.
Fig. 1 is a comparison graph before and after load optimization by the peak-valley load optimization method based on user demand elastic response and time-of-use electricity price provided in the embodiment of the present invention;
in the figure, A represents before optimization, and B represents after optimization;
Detailed Description
First, it should be noted that the specific structures, features, advantages, etc. of the present invention will be specifically described below by way of example, but all the descriptions are for illustrative purposes only and should not be construed as limiting the present invention in any way. Furthermore, any single feature described or implicit in any embodiment or any single feature shown or implicit in any drawing may still be combined or subtracted between any of the features (or equivalents thereof) to obtain still further embodiments of the invention that may not be directly mentioned herein.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The peak-valley load optimization method based on the user demand elastic response and the time-of-use electricity price provided by the embodiment comprises the following steps:
acquiring load and real-time electricity price data of a power system;
determining the peak-valley membership degree of the load, carrying out peak-valley average time interval division on the load to obtain a user elastic response matrix, and establishing a demand response model of the user load;
constructing a peak-valley time-of-use electricity price and load optimization model taking the minimum system peak-valley difference as a target function, wherein the constraint conditions are respectively constant total demand of electricity load, peak-valley electricity price constraint and income constraint of a user and a power supply company;
and (3) setting appropriate parameters by adopting a genetic algorithm, determining appropriate electricity prices at peak-valley time periods, and optimizing peak-valley load.
Specifically, determining peak-valley membership of the load, performing peak-valley average time division of the load to obtain a user elastic response matrix, and establishing a demand response model of the user load, wherein the steps of the method comprise:
(1) determining the maximum load, the minimum load, the peak-valley difference, the load rate and the peak-valley difference rate according to the obtained load data:
a＝minai
b＝maxai
d＝b-a
wherein a and b are respectively the minimum and maximum load, aiLoad at time i, d is the peak-to-valley difference, r1Is the load factor, r2The peak-to-valley difference rate;
(2) determining the peak-valley membership degree of the load, and carrying out standardization:
the membership degree of peak and valley time sections is as follows:
in the formula, xi，1Degree of membership, x, of the load with respect to the peak segment at time ii，2The membership degree of the load related to the valley section at the ith moment;
and (3) carrying out standardization treatment:
in formula (II), x'i,kTaking 1 and 2 as k to respectively represent peak section and valley section for the normalized membership degree of the peak section and the valley section, and xavg,kAverage value of degree of membership of load to crest or trough at all times of day, skThe standard deviation of the load on the membership degree of the peak section or the valley section at all the moments in one day;
(3) dividing peak-valley average time periods according to the peak-valley membership:
determining a threshold value theta1,θ2Comparing the peak-to-valley membership to an initial threshold value when x'i,1＜θ1When i belongs to the valley period, when θ1≤x'i,1＜θ2When i belongs to the period of flatness, when x'i,1≥θ2When i belongs to the peak period.
Wherein the threshold value theta1,θ2Determining according to the evaluation index S of the peak-valley average division effect:
wherein S is an index for evaluating the effect of peak-to-valley leveling, Pmax,Pmin,Vmax,Vmin,Mmax,MminMaximum and minimum load values within the peak, valley, plateau interval, respectively, are determined by:
maxS(f(θ1,θ2))
s.t(θ1,θ2)∈[0,1]
solve for the threshold value theta1,θ2Obtaining peak-valley average time interval division;
(4) acquiring the electric power demand elasticity coefficient and the load change rate of a user:
elastic coefficient of power demand:
in the formula, i and j take 1, 2 and 3 to respectively represent three periods of peak-to-valley level,ijfor the elastic coefficient of the impact of the change in electricity price corresponding to the period j on the load demand of the period i, qi、ΔqiRespectively the load demand and the load variation corresponding to the i period,pj、Δpjelectricity price and electricity price variation, Q, respectively, corresponding to the j time periodiThe electric load quantity, Q, corresponding to the i time period after the time-of-use electricity price is implementedi0The load amount of the corresponding time interval before the time-of-use electricity price is implemented;
load change rate of the user in the i period:
λii＝ii
in the formula, Pi0For applying electricity prices at corresponding time periods before time-of-use electricity prices, PiFor applying the electricity prices at the corresponding time intervals after the time-of-use electricity prices, KiTo implement the time-of-use electricity rate, i.e. the floating rate of the electricity rate, lambda, of the user during the period iiRepresents the user load change rate, lambda, before and after the user carries out the time-of-use electricity priceiiFor the rate of reduction/increase of the stiffness load of the user, lambdaij(i ≠ j) is the non-rigid load transfer rate of the user;
(5) determining a load demand response model based on a user demand elasticity matrix:
the user elastic response matrix is:
electric load demand response model:
in the formula, Q10、Q20、Q30Respectively the load of the user in each time interval at peak-valley level before the time-of-use electricity price is implemented, lambdaijAs rate of change of user load, T1、T2、T3Representing three periods of peak-to-valley, respectively.
And constructing a peak-valley time-of-use electricity price and load optimization model taking the minimum system peak-valley difference as a target function:
F＝min(maxQi-minQi),(i＝1,2,...,24)
in the formula, maxQiAt the maximum value of the system load, minQiIs the minimum value of the system load;
the constraint conditions are respectively the constant electricity load, the constraint of electricity price, the benefit of users and the benefit of power supply companies:
and (3) power load invariant constraint:
Q1+Q2+Q3＝Q10+Q20+Q30
in the formula, Q1、Q2、Q3Respectively the peak, valley and ordinary period load after the time-of-use electricity price is implemented, Q10、Q20、Q30Respectively the peak, valley and ordinary time load of the user before the time-of-use electricity price is implemented;
and (4) electricity price constraint:
in the formula, P1、P2、P3Electricity prices, P, in peak, valley, flat periods, respectively1min、P2min、P3min、P1max、P2max、P3maxThe lower limit and the upper limit of the price change in three periods of time respectively, x is the price change proportion, k is the price ratio change range, m1、m2Respectively peak-to-valley periodLower and upper limits of the valence ratio, P0The electricity price before the implementation of the time-of-use electricity price; china stipulates as m1＝2、m2＝5。
User and power company benefit constraints:
M0＝P0(Q10+Q20+Q30)
M1＝P1 Q1+P2 Q2+P3 Q3
M1≤M0
M1≥M0-M′
in the formula, M0、M1The electric charge expenditure of the user before and after implementing the time-of-use electricity price and the electricity selling income of the power supply company are respectively, and M' is the capacity investment saved due to the reduction of the load of implementing the time-of-use electricity price.
The optimization model is as follows:
further, solving a peak-valley time-of-use electricity price and load optimization model taking the minimum system peak-valley difference as a target function by adopting a genetic algorithm to obtain an optimized peak-valley load; specifically, the method comprises the following steps:
determining the parameters and specific values needed in the initialization procedure: the population scale is set to 100; the frequency of crossover probability control switching operation is set to 0.9; the mutation probability is set to be 0.1, and the maximum evolution algebra is taken as 100;
coding with decimal system to ensure that the average power rate of user is P before peak-valley time-of-use power rate is implemented in the range interval specified by the parametersavgRatio x varying on the basis of average electricity price in three periods of peak-to-valley1、x2、x3Is variable X ═ X1、x2、x3)TCoding the parameters by decimal system, the precision reaches 4 bits after decimal point, the dyeing string body is composed of decimal string parameters of three parameters to form an individual, each decimal string parameter is coded by decimal systemAll parameters are selected from 0 to 9, the population size M is set, M chromosome strings are formed, and an initial population is established.
Using the inverse of the objective function as the fitness function:
in the formula, F (x)i,k) K is an algebraic number;
selecting individuals inherited to the next generation according to the probability that the individual fitness function value is in direct proportion by using a roulette method; and randomly selecting two cross points, selecting two chromosome strings which are matched with each other according to the cross probability, and exchanging gene strings of the two points so as to generate a new individual. Then randomly selecting one value m of a chromosome string according to the mutation probability, and replacing the original value with 9-m to generate a new individual. This process is repeated until the maximum number of iterations is 100 or an optimal solution is obtained.
For example, in the present embodiment, a peak-to-valley load optimization method based on the user demand elastic response and the time-of-use electricity price, taking a typical daily load of a certain area as an example, is adopted, and the typical daily load data of the certain area is shown in table 1 below:
TABLE 1 typical daily load data for a certain area
| Time point | Load, MW | Time point | Load, MW |
| 1 | 3940 | 13 | 5270 |
| 2 | 4110 | 14 | 5340 |
| 3 | 4020 | 15 | 5320 |
| 4 | 3890 | 16 | 5300 |
| 5 | 4190 | 17 | 5620 |
| 6 | 4370 | 18 | 5680 |
| 7 | 4600 | 19 | 6010 |
| 8 | 4970 | 20 | 5990 |
| 9 | 5540 | 21 | 5640 |
| 10 | 5660 | 22 | 5260 |
| 11 | 5810 | 23 | 4490 |
| 12 | 5180 | 24 | 4260 |
From the above data, one can obtain: the maximum load of the system is 6010 MW; the minimum load is 3890 MW; the peak-to-valley difference is 2220 MW; the peak-to-valley difference rate is 36.39%; the load factor is 83.51%;
dividing the peak-valley time period, and calculating the peak-valley membership degree of each time point according to a fuzzy membership function, wherein the peak-valley membership degree is expressed as follows:
TABLE 2 degree of membership between peaks and valleys
Clustering according to the peak-valley membership degree in the table, and determining a threshold value theta when the classification number is 31＝0.32,θ20.50 to obtainBasic peak, valley, plateau time division:
the average value of the peak-valley electricity price is 0.5246 yuan/kw.h;
peak-to-valley electricity price variation ratio constraint range:
user response flexibility:
according to the original data, setting operation parameters, wherein the maximum population scale is 100, the cross probability is 0.9, the variation probability is 0.1, the maximum iteration number is 100, and the result of model optimization is as follows:
TABLE 3 optimization results
The optimized peak-section electricity price obtained by the method is 0.7580 yuan/KW.h, the flat-section electricity price is 0.5508 yuan/KW.h, and the valley-section electricity price is 0.2481 yuan/KW.h, the distribution of user load is guided by the time-of-use electricity price, and the optimized load curve is shown in figure 1;
based on the peak-valley load optimization method based on the user demand elastic response and the time-of-use electricity price, the user load is optimized and the load peak-valley difference is reduced through the reasonable time-of-use electricity price, compared with the traditional policy method, the maximum load is reduced from the original 6010MW to 5707.39MW, the minimum load is increased from 3890MW to 4169.98MW, the peak-valley difference is reduced to 1537.41MW, the peak-valley difference rate is reduced from 36.39% to 26.94%, and the system load rate is increased by 5.66%. The method provided by the invention is beneficial to reducing the pressure of the power system, fully utilizes the existing power equipment, optimizes the power resource allocation, reduces the investment cost of the power system and has important significance for improving the stability of the system.
The present invention has been described in detail with reference to the above examples, but the description is only for the preferred examples of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (5)
1. The peak-valley load optimization method based on the user demand elastic response and the time-of-use electricity price is characterized by comprising the following steps of:
acquiring load and real-time electricity price data of a power system;
determining the peak-valley membership degree of the load, carrying out peak-valley average time interval division on the load to obtain a user elastic response matrix, and establishing a demand response model of the user load;
constructing a peak-valley time-of-use electricity price and load optimization model taking the minimum system peak-valley difference as a target function, wherein the constraint conditions are respectively constant total demand of electricity load, peak-valley electricity price constraint and income constraint of a user and a power supply company;
and (4) obtaining the optimized time-of-use electricity price by adopting a genetic algorithm, and optimizing peak-valley load.
2. The peak-to-valley load optimization method based on user demand elastic response and time-of-use electricity prices according to claim 1, characterized in that: determining the peak-valley membership degree of the load, dividing the peak-valley average time period of the load to obtain a user elastic response matrix, and establishing a demand response model of the user load, wherein the steps of the method comprise the following steps:
(1) determining the maximum load, the minimum load, the peak-valley difference, the load rate and the peak-valley difference rate according to the obtained load data:
a＝minai
b＝maxai
d＝b-a
wherein a and b are respectively the minimum and maximum load, aiLoad at time i, d is the peak-to-valley difference, r1Is the load factor, r2The peak-to-valley difference rate;
(2) determining the peak-valley membership degree of the load, and carrying out standardization:
the membership degree of peak and valley time sections is as follows:
in the formula, xi，1Degree of membership, x, of the load with respect to the peak segment at time ii，2The membership degree of the load related to the valley section at the ith moment;
and (3) carrying out standardization treatment:
in formula (II), x'i,kTaking 1 and 2 as k to respectively represent peak section and valley section for the normalized membership degree of the peak section and the valley section, and xavg,kAverage value of degree of membership of load to crest or trough at all times of day, skThe standard deviation of the load on the membership degree of the peak section or the valley section at all the moments in one day;
(3) dividing peak-valley leveling time periods according to the peak-valley membership;
(4) acquiring the electric power demand elasticity coefficient and the load change rate of a user:
elastic coefficient of power demand:
in the formula, i and j take 1, 2 and 3 to respectively represent three periods of peak-to-valley level,ijfor the elastic coefficient of the impact of the change in electricity price corresponding to the period j on the load demand of the period i, qi、ΔqiLoad demand and load variation, p, respectively, for period ij、ΔpjElectricity price and electricity price variation, Q, respectively, corresponding to the j time periodiThe electric load quantity, Q, corresponding to the i time period after the time-of-use electricity price is implementedi0The load amount of the corresponding time interval before the time-of-use electricity price is implemented;
load change rate of the user in the i period:
λii＝ii
in the formula, Pi0For applying electricity prices at corresponding time periods before time-of-use electricity prices, PiFor applying the electricity prices at the corresponding time intervals after the time-of-use electricity prices, KiTo implement the rate of change of electricity prices, λ, of the user during the period i after the time of use of electricity pricesiRepresents the user load change rate, lambda, before and after the user carries out the time-of-use electricity priceiiFor the rate of reduction/increase of the stiffness load of the user, lambdaij(i ≠ j) is the non-rigid load transfer rate of the user;
(5) determining a load demand response model based on a user demand elasticity matrix:
the user elastic response matrix is:
electric load demand response model:
in the formula, Q10、Q20、Q30Respectively the load of the user in each time interval at peak-valley level before the time-of-use electricity price is implemented, lambdaijAs rate of change of user load, T1、T2、T3Representing three periods of peak-to-valley, respectively.
3. The peak-to-valley load optimization method based on user demand elastic response and time-of-use electricity prices according to claim 2, characterized in that: constructing a peak-valley time-of-use electricity price and load optimization model taking the minimum system peak-valley difference as a target function:
F＝min(maxQi-minQi),(i＝1,2,...,24)
in the formula, maxQiAt the maximum value of the system load, minQiIs the minimum value of the system load;
the constraint conditions are respectively the constant electricity load, the constraint of electricity price, the benefit of users and the benefit of power supply companies:
and (3) power load invariant constraint:
Q1+Q2+Q3＝Q10+Q20+Q30
in the formula, Q1、Q2、Q3Respectively the peak, valley and ordinary period load after the time-of-use electricity price is implemented, Q10、Q20、Q30Respectively the peak, valley and ordinary time load of the user before the time-of-use electricity price is implemented;
and (4) electricity price constraint:
in the formula, P1、P2、P3Electricity prices, P, in peak, valley, flat periods, respectively1min、P2min、P3min、P1max、P2max、P3maxThe lower limit and the upper limit of the price change in three periods of time respectively, x is the price change proportion, k is the price ratio change range, m1、m2Lower limit and upper limit of peak-to-valley period, P0The electricity price before the implementation of the time-of-use electricity price;
user and power company benefit constraints:
M0＝P0(Q10+Q20+Q30)
M1＝P1Q1+P2Q2+P3Q3
M1≤M0
M1≥M0-M′
in the formula, M0、M1The electric charge expenditure of the user before and after implementing the time-of-use electricity price and the electricity selling income of the power supply company are respectively, and M' is the capacity investment saved due to the reduction of the load of implementing the time-of-use electricity price.
4. The peak-to-valley load optimization method based on user demand elastic response and time-of-use electricity prices according to claim 3, characterized in that: and solving the peak-valley time-of-use electricity price and load optimization model which takes the minimum system peak-valley difference as a target function by adopting a genetic algorithm to obtain the optimized time-of-use electricity price and optimize the peak-valley load.
5. The peak-to-valley load optimization method based on user demand elastic response and time-of-use electricity prices according to claim 2, characterized in that: the method for dividing the peak-valley average time period according to the peak-valley membership comprises the following steps:
determining a threshold value theta1,θ2Comparing the peak-to-valley membership to an initial threshold value when x'i,1＜θ1When i belongs to the valley period, when θ1≤x'i,1＜θ2When i belongs to the period of flatness, when x'i,1≥θ2When i belongs to a peak period;
wherein the threshold value theta1,θ2Determining according to the evaluation index S of the peak-valley average division effect:
wherein S is an index for evaluating the effect of peak-to-valley leveling, Pmax,Pmin,Vmax,Vmin,Mmax,MminMaximum and minimum load values within the peak, valley, plateau interval, respectively, are determined by:
max S(f(θ1,θ2))
s.t(θ1,θ2)∈[0,1]
solve out the threshold valueθ1,θ2And obtaining peak-to-valley average time interval division.
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