CN104573313A - Acquisition method and acquisition system of customer response model under peak-valley time-of-use price - Google Patents

Abstract

The invention provides an acquisition method and an acquisition system of a customer response model under a peak-valley time-of-use price. The acquisition method comprises the following steps of representing peak-valley time-of use price level and preset other factors influencing a customer response behavior by using fuzzy numbers, and determining influence factor comprehensive indexes of the customer response behavior according to the peak-valley time-of-use price level and the weight of the preset other factors influencing the customer response behavior; establishing a linear regression model of the customer response behavior according to the influence factor comprehensive indexes; acquiring multiple groups of influence factors and an observed value of the electric quantity of customer response, and determining response parameters in the linear regression model according to the observed value through a fuzzy adaptive least square method; determining the customer response model under the peak-valley time-of-use price according to the response parameters. The acquisition method and the acquisition system are conductive to improving the accuracy and effectiveness of the customer response model under the peak-valley time-of-use price and providing reliable evidences for network load control, thus generating remarkable social, economic and environmental benefits.

Description

The acquisition methods of the customer response model under Peak-valley TOU power price and system

[technical field]

The present invention relates to network load regulation and control field, the acquisition methods of the customer response model particularly under a kind of Peak-valley TOU power price and system.

[background technology]

The mode of the electricity consumption behavior of user is guided by Peak-valley TOU power price, it is the important way of carrying out network load regulation and control, obtain extensive concern in countries in the world at present, and be applied in provinces such as the developed countries such as America and Europe and domestic Guangdong, Jiangsu, Zhejiang.In order to formulate Peak-valley TOU power price better, existing scholar carries out modeling analysis to the respondent behavior of user under Peak-valley TOU power price.Existing method mainly comprises the method for Corpus--based Method principle, the method based on consumer psychology and the method based on Power demand price elasticity coefficient.When applying these methods, general supposition all types of user response model is known, and carries out the Optimal Decision-making of tou power price on this basis.Due to the complicacy of user group, the respondent behavior of user is difficult to carry out accurate description by the parameter of supposition, a kind of more rational method is that the actual measurement load data utilizing Peak-valley TOU power price to implement front and back carries out quantitative analysis to the response parameter of user under different model, but up to the present, relevant this kind of technical research is still fewer.

In the quantitative analysis of the user's response parameter under existing tou power price, there is a kind of method to be that the deviation of surveying load and matching load after implementing tou power price is minimised as target, adopt the response model parameter of least square method to load to estimate.On this basis, there is scholar to propose to utilize the method for the true responsiveness curve of user further, solve piecewise linearity responsiveness point of inflexion on a curve process problem.But the method for parameter estimation of user's respondent behavior is deterministic method under existing Peak-valley TOU power price, mainly there is the problem of following three aspects in this:

(1) larger to the dependence of data.Deterministic method only discusses accurate data, but the load responding data of reality are difficult to Obtaining Accurate, usually there is the situation such as shortage of data or mistake, this also makes the application performance of the Deterministic Methods that user's response parameter is estimated under Peak-valley TOU power price be subject to larger impact.

(2) the uncertain respondent behavior of user cannot be described comprehensively.The electricity consumption behavior of user is by the impact of multiple uncertain factor, and under a certain tou power price, the respondent behavior of user is also larger uncertain by existing.Adopt deterministic method for parameter estimation can only describe user's qualitative answer feature really, larger difference may be produced with actual conditions.

(3) error of fitting of parameter estimation is larger.Under existing Peak-valley TOU power price, the method for parameter estimation of user's response mainly adopts least square method, because the randomness of user's respondent behavior is larger, under a certain electricity price user to respond the multi-group data difference of electricity also larger, be thus difficult to obtain good fitting effect.

[summary of the invention]

Based on this, the invention provides the acquisition methods of the customer response model under a kind of Peak-valley TOU power price, solve the problem that the dependence to data that user's respondent behavior analytical approach exists under existing Peak-valley TOU power price is comparatively large, the error of fitting of the uncertain respondent behavior that comprehensively cannot describe user, parameter estimation is larger, for the load control of electrical network provides important evidence.

The content of the embodiment of the present invention is specific as follows:

An acquisition methods for customer response model under Peak-valley TOU power price, comprises the steps:

Utilize the factor of other influences user respondent behavior that fuzzy number represents Peak-valley TOU power price level and presets, and according to described Peak-valley TOU power price level and described in the weight of the factor of other influences user respondent behavior that presets determine the influence factor overall target of user's respondent behavior;

The linear regression model (LRM) of user's respondent behavior is set up according to described influence factor overall target;

Obtain Peak-valley TOU power price level described in some groups, described in the factor of other influences user respondent behavior that presets and user respond the observed reading of electricity, determine the response parameter in described linear regression model (LRM) according to described observed reading by fuzzy least squares method;

The customer response model under the Peak-valley TOU power price of network load regulation and control is determined according to described response parameter.

Accordingly, the present invention also provides the acquisition system of the customer response model under a kind of Peak-valley TOU power price, comprising:

Overall target computing module, for the factor of other influences user respondent behavior utilizing fuzzy number to represent Peak-valley TOU power price level and preset, and according to described Peak-valley TOU power price level and described in the weight of the factor of other influences user respondent behavior that presets determine the influence factor overall target of user's respondent behavior;

MBM, for setting up the linear regression model (LRM) of user's respondent behavior according to described influence factor overall target;

Observed reading acquisition module, for obtain described Peak-valley TOU power price level, described in the factor of other influences user respondent behavior that presets and user respond the observed reading of electricity;

Parametric solution module, for determining the response parameter in described linear regression model (LRM) according to described observed reading by fuzzy least squares method;

Response model acquisition module, for determine according to described response parameter network load regulation and control Peak-valley TOU power price under customer response model.

The theory that fuzzy number is estimated is incorporated in the research of user's respondent behavior under Peak-valley TOU power price by the present invention, and adopts fuzzy least squares method to solve.In power grid new technology, method provided by the invention can combine with artificial experience easily, under partial data disappearance or inaccurate situation, the respondent behavior of user is effectively analyzed, the stochastic problems of the electricity consumption behavior of various user group can be considered again comprehensively, form the comprehensive description to user's respondent behavior under Peak-valley TOU power price, the variability issues of user's respondent behavior under same time-of-use price strategy can also be considered, be conducive to the accuracy and the validity that improve customer response model.In economic results in society, customer response model under the Peak-valley TOU power price that the present invention obtains can control to provide reliable basis for the formulation of electricity price and network load, for energy-saving and emission-reduction power generation dispatching provides good condition, and the unbalanced supply-demand problem of the initiation because randomness strengthens after solving new forms of energy access from load side, thus produce significant society, economy and environment benefit.

[accompanying drawing explanation]

Fig. 1 is the schematic flow sheet of the acquisition methods of customer response model in the embodiment of the present invention under Peak-valley TOU power price;

Fig. 2 is the structural representation of the acquisition system of customer response model in the embodiment of the present invention under Peak-valley TOU power price;

Fig. 3 is the structural representation of parametric solution module in the embodiment of the present invention.

[embodiment]

Below in conjunction with specific embodiment, content of the present invention is further elaborated.

As shown in Figure 1, the present embodiment provides the acquisition methods of the customer response model under a kind of Peak-valley TOU power price, comprises the following steps:

The factor of other influences user respondent behavior that S1 utilizes fuzzy number to represent Peak-valley TOU power price level and preset, and according to described Peak-valley TOU power price level and described in the weight of the factor of other influences user respondent behavior that presets determine the influence factor overall target of user's respondent behavior;

S2 sets up the linear regression model (LRM) of user's respondent behavior according to described influence factor overall target;

S3 obtain Peak-valley TOU power price level described in some groups, described in the factor of other influences user respondent behavior that presets and user respond the observed reading of electricity, determine the response parameter in described linear regression model (LRM) according to described observed reading by fuzzy least squares method;

S4 determines the customer response model under the Peak-valley TOU power price of network load regulation and control according to described response parameter.

Concrete, when implementing Peak-valley TOU power price, the respondent behavior (also can be referred to as electricity consumption behavior) of user changes with the change of electricity price, therefore the respondent behavior of user is the most remarkable by the impact of Peak-valley TOU power price level.But the respondent behavior that it should be noted that user also with climatic environment, work relevant with the factor such as living arrangement, such as the highest temperature, relative humidity, day operating time etc.Therefore, except emphasis considers Peak-valley TOU power price level, the impact taking into account other factors is also needed.And the determining step of customer response model mainly sets up an input-output model according to the influence factor of user's respondent behavior and the variation relation of power consumption, in order to describe the above-mentioned influence factor comprising Peak-valley TOU power price level situation is affected on user power utilization amount.

According to Peak-valley TOU power price level and preset other affect the factor determination influence factor overall target X of user's respondent behavior, because various influence factor has larger randomness, the present embodiment adopts Trapezoid Fuzzy Number to be described X, and other fuzzy numbers are as also passable in Triangular Fuzzy Number certainly.Now X can be described as:

$X=\underset{j=1}{\overset{k}{\mathrm{\Σ}}}{\mathrm{\ω}}_j{p}_j$

Wherein, p _jfor a jth influence factor, available ladder type fuzzy number represents; ω _jfor the weight of a jth influence factor, consider when applying Peak-valley TOU power price, Peak-valley TOU power price level is the most remarkable on the impact of user's respondent behavior, and therefore in the present embodiment, the weight of Peak-valley TOU power price level gets 0.7; K is the sum of influence factor.

Suppose that the response electricity of user under a certain Peak-valley TOU power price level is Q, then Q also can adopt Trapezoid Fuzzy Number to be described due to its randomness.Now, the respondent behavior of user can be expressed as Q=a+bX by linear regression model (LRM), wherein, and response parameter a >=0, b≤0, this linear regression model (LRM) reflects that the various influence factors comprising Peak-valley TOU power price level affect situation to user's respondent behavior.

In order to describe the respondent behavior of user under different situations qualitatively, need the data of many group X and Q, therefore, obtain some groups of Peak-valley TOU power price levels and preset other affect the observed reading of the factor of user's respondent behavior, thus obtain the observed reading of X, and the observed reading that user responds electricity Q can directly obtain, then according to the observed reading of many group X and Q, by fuzzy least squares method calculated response parameter a, b.

The present embodiment provides a kind of concrete computing method.Suppose there is n group observations X _i, Y _i, i=1 ..., n, because they are all Trapezoid Fuzzy Number, can be expressed as $X_i=(x_i^{\left(1\right)},x_i^{\left(2\right)},x_i^{\left(3\right)},x_i^{\left(4\right)}),Q_i=(q_i^{\left(1\right)},q_i^{\left(2\right)},q_i^{\left(3\right)},q_i^{\left(4\right)}),$ Adopt fuzzy least squares method, the estimation problem of response parameter a, b can be converted into following optimization problem:

min r(a,b)

Namely the minimum value of response parameter estimation function r (a, b) is asked for, wherein

$r(a,b)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\left[{({(a+{\mathrm{bx}}_i^{\left(1\right)}-q_i^{\left(1\right)})}^2+{(a+{\mathrm{bx}}_i^{\left(2\right)}-q_i^{\left(2\right)})}^2+{(a+{\mathrm{bx}}_i^{\left(3\right)}-q_i^{\left(3\right)})}^2+{(a+{\mathrm{bx}}_i^{\left(4\right)}-q_i^{\left(4\right)})}^2)}^2\right]$

The process solving response parameter estimation function is as follows:

The point that response parameter estimation function partial derivative equals 0 is extreme point, therefore, and order have

$4\mathrm{na}+b\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(x_i^{\left(1\right)}+x_i^{\left(2\right)}+x_i^{\left(3\right)}+x_i^{\left(4\right)})=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(q_i^{\left(1\right)}+q_i^{\left(2\right)}+q_i^{\left(3\right)}+q_i^{\left(4\right)})$

$\begin{array}{c}a\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(x_i^{\left(1\right)}+x_i^{\left(2\right)}+x_i^{\left(3\right)}+x_i^{\left(4\right)})+b\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\left(x_i^{\left(1\right)}\right)}^2+{\left(x_i^{\left(2\right)}\right)}^2+{\left(x_i^{\left(3\right)}\right)}^2+{\left(x_i^{\left(4\right)}\right)}^2)\\ =\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(x_i^{\left(1\right)}{q}_i^{\left(4\right)}+x_i^{\left(4\right)}{q}_i^{\left(1\right)}+x_i^{\left(2\right)}{q}_i^{\left(3\right)}+x_i^{\left(3\right)}{q}_i^{\left(2\right)})\end{array}$

Solve

$a=\frac{1}{4}f\left(q\right)+\frac{1}{4}\mathrm{bf}\left(x\right)$

$b=\frac{4\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(x_i^{\left(1\right)}{q}_i^{\left(4\right)}+x_i^{\left(4\right)}{q}_i^{\left(1\right)}+x_i^{\left(2\right)}{q}_i^{\left(3\right)}+x_i^{\left(3\right)}{q}_i^{\left(2\right)})-\mathrm{nf}\left(x\right)f\left(q\right)}{4\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[{\left(x_i^{\left(1\right)}\right)}^2+{\left(x_i^{\left(2\right)}\right)}^2+{\left(x_i^{\left(3\right)}\right)}^2+{\left(x_i^{\left(4\right)}\right)}^2]-n{\left(f\left(x\right)\right)}^2}$

Wherein

$f\left(x\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(x_i^{\left(1\right)}+x_i^{\left(2\right)}+x_i^{\left(3\right)}+x_i^{\left(4\right)})$

$f\left(q\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(q_i^{\left(1\right)}+q_i^{\left(2\right)}+q_i^{\left(3\right)}+q_i^{\left(4\right)})$

Solve response parameter a, after b, substituted into above-mentioned linear regression model (LRM) Q=a+bX, the customer response model under final Peak-valley TOU power price can be obtained.The formulation that this customer response model can be electricity price provides foundation, utilizes this customer response model effectively can regulate and control network load.

In sum, the present invention is while consideration Peak-valley TOU power price level is on the impact of user's respondent behavior, also set up an influence factor overall target to take into account the impact of other influence factor, uncertain problem is considered in the response parameter estimation of the user's respondent behavior under Peak-valley TOU power price, and solved by fuzzy least squares method, be conducive to the accuracy and the validity that improve customer response model.Customer response model under the Peak-valley TOU power price obtained by the present invention can control to provide reliable basis for network load, for energy-saving and emission-reduction power generation dispatching provides good condition, and the unbalanced supply-demand problem of the initiation because randomness strengthens after solving new forms of energy access from load side, thus produce significant society, economy and environment benefit.

Accordingly, the present invention also provides the acquisition system of the customer response model under a kind of Peak-valley TOU power price, as shown in Figure 2, comprising:

Overall target computing module 1, for the factor of other influences user respondent behavior utilizing fuzzy number to represent Peak-valley TOU power price and preset, and according to described Peak-valley TOU power price and described in the weight of the factor of other influences user respondent behavior that presets determine the influence factor overall target of user's respondent behavior;

MBM 2, for setting up the linear regression model (LRM) of user's respondent behavior according to described influence factor overall target;

Observed reading acquisition module 3, for obtain described Peak-valley TOU power price level, described in the factor of other influences user respondent behavior that presets and user respond the observed reading of electricity;

Parametric solution module 4, for determining the response parameter in described linear regression model (LRM) according to described observed reading by fuzzy least squares method;

Response model acquisition module 5, for determine according to described response parameter network load regulation and control Peak-valley TOU power price under customer response model.

Concrete, overall target computing module 1 sets an influence factor overall target, because under the environment implementing Peak-valley TOU power price, the respondent behavior of user changes with the change of electricity price, therefore the respondent behavior of user is the most remarkable by the impact of Peak-valley TOU power price level, but the respondent behavior of user also with climatic environment, work relevant with the factor such as living arrangement, such as the highest temperature, relative humidity, day operating time etc.For taking into account the impact of other factors.Overall target computing module 1 according to Peak-valley TOU power price level and preset other affect the factor of user's respondent behavior and respective weight sets up influence factor overall target.Because the factor of the various user's of impact respondent behavior all has larger randomness, overall target computing module 1 adopts fuzzy number to represent various influence factor and influence factor overall target.

MBM 2 mainly sets up an input-output model according to the influence factor of user's respondent behavior and the variation relation of power consumption, affects situation in order to describe these influence factors to the power consumption of user.Its specific practice is: the influence factor overall target calculated by overall target computing module 1 is as independent variable, and user's respondent behavior, as dependent variable, sets up the linear regression model (LRM) of user's respondent behavior.

Then, Peak-valley TOU power price level is inputted to observed reading acquisition module 3, the factor of the described other influences user respondent behavior preset and user respond the observed reading of electricity, the observed reading that parametric solution module 4 just can utilize observed reading acquisition module 3 to export the response parameter calculated by fuzzy least squares method in linear regression model (LRM), the response parameter that parametric solution module 4 exports by response model acquisition module 5 substitutes into the linear regression model (LRM) generated in MBM 2, can obtain final for the customer response model under the Peak-valley TOU power price of network load regulation and control.

In a kind of embodiment, as shown in Figure 3, parametric solution module 4 comprises:

Function sets up module 41, for setting up response parameter estimation function according to described observed reading by fuzzy least squares method;

Solve module 42, for the response parameter solved in the described linear regression model (LRM) of acquisition by described response parameter estimation function minimum value.

For calculated response parameter, function is set up module 41 and is set up response parameter estimation function by the observed reading of fuzzy least squares method and observed reading acquisition module 3 acquisition, response parameter estimation problem is converted into the minimum value asking for response parameter estimation function, then utilize the minimum problems solving module 42 pairs of response parameter estimation functions to solve, thus obtain response parameter.

In a kind of embodiment, described in the factor of other influences user respondent behavior that presets comprise the highest temperature, relative humidity, day operating time.

In a kind of embodiment, consider that Peak-valley TOU power price level is the most remarkable on the impact of user's respondent behavior, therefore, the weight that overall target computing module 1 sets Peak-valley TOU power price level is 0.7.

In the acquisition system of the customer response model under the present embodiment Peak-valley TOU power price, the realization of its concrete function of modules, can refer to above-mentioned method, repeats no longer one by one herein.

The above embodiment only have expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but therefore can not be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.

Claims (8)

1. an acquisition methods for the customer response model under Peak-valley TOU power price, is characterized in that, comprises the steps:

Utilize the factor of other influences user respondent behavior that fuzzy number represents Peak-valley TOU power price level and presets, and according to described Peak-valley TOU power price level and described in the weight of the factor of other influences user respondent behavior that presets determine the influence factor overall target of user's respondent behavior;

The linear regression model (LRM) of user's respondent behavior is set up according to described influence factor overall target;

Obtain Peak-valley TOU power price level described in some groups, described in the factor of other influences user respondent behavior that presets and user respond the observed reading of electricity, determine the response parameter in described linear regression model (LRM) according to described observed reading by fuzzy least squares method;

The customer response model under the Peak-valley TOU power price of network load regulation and control is determined according to described response parameter.

2. the acquisition methods of the customer response model under Peak-valley TOU power price according to claim 1, is characterized in that, the process of the response parameter determined in described linear regression model (LRM) according to described observed reading and by fuzzy least squares method comprises the steps:

Response parameter estimation function is set up by fuzzy least squares method according to described observed reading;

The response parameter obtained in described linear regression model (LRM) is solved by the minimum value of described response parameter estimation function.

3. the acquisition methods of the customer response model under Peak-valley TOU power price according to claim 1, is characterized in that, the factor of described other influences user respondent behavior comprise the highest temperature, relative humidity, every day operating time.

4. the acquisition methods of the customer response model under the Peak-valley TOU power price according to claim 1 or 2 or 3, is characterized in that, the weight of described Peak-valley TOU power price level is 0.7.

5. an acquisition system for the customer response model under Peak-valley TOU power price, is characterized in that, comprising:

Overall target computing module, for the factor of other influences user respondent behavior utilizing fuzzy number to represent Peak-valley TOU power price level and preset, and according to described Peak-valley TOU power price level and described in the weight of the factor of other influences user respondent behavior that presets determine the influence factor overall target of user's respondent behavior;

MBM, for setting up the linear regression model (LRM) of user's respondent behavior according to described influence factor overall target;

Observed reading acquisition module, for obtain described Peak-valley TOU power price level, described in the factor of other influences user respondent behavior that presets and user respond the observed reading of electricity;

Parametric solution module, for determining the response parameter in described linear regression model (LRM) according to described observed reading by fuzzy least squares method;

Response model acquisition module, for determine according to described response parameter network load regulation and control Peak-valley TOU power price under customer response model.

6. the acquisition system of the customer response model under Peak-valley TOU power price according to claim 5, is characterized in that, described parametric solution module comprises:

Function sets up module, for setting up response parameter estimation function according to described observed reading by fuzzy least squares method;

Solve module, for being solved the response parameter obtained in described linear regression model (LRM) by the minimum value of described response parameter estimation function.

7. the acquisition system of the customer response model under Peak-valley TOU power price according to claim 5, it is characterized in that, the factor of the described other influences user respondent behavior that described overall target computing module obtains comprise the highest temperature, relative humidity, every day operating time.

8. the acquisition system of the customer response model under the Peak-valley TOU power price according to claim 5 or 6 or 7, is characterized in that, the weight of described Peak-valley TOU power price level is 0.7.