CN112016977A

CN112016977A - Method and system for calculating and acquiring electricity consumption information with stepped electricity price optimization model and electricity quantity data server

Info

Publication number: CN112016977A
Application number: CN202010922093.9A
Authority: CN
Inventors: 侯国强; 赵信华; 段伦峰; 韩涛; 赵勇; 李秀红; 董子郁; 李春彦; 亓立红; 刘松; 李娜; 郭红军; 吕波; 高森; 亓晓娟; 葛华
Original assignee: State Grid Corp of China SGCC; Laiwu Power Supply Co of State Grid Shandong Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; Laiwu Power Supply Co of State Grid Shandong Electric Power Co Ltd
Priority date: 2020-09-04
Filing date: 2020-09-04
Publication date: 2020-12-01
Anticipated expiration: 2040-09-04
Also published as: CN112016977B

Abstract

The invention provides a method and a system for collecting electricity consumption information and an electric quantity data server with a step electricity price optimization model, wherein the step electricity price grading grade model is configured based on a rank and ratio method; configuring a grading electric quantity processing model; configuring a grading electricity price processing model; and processing and displaying the stepped electricity price electric quantity based on the stepped electricity price grading level model, the graded electric quantity processing model and the graded electricity price processing model. And measuring the electricity charge of the electricity consumer based on the grading grade optimization model, the grading electricity quantity and the grading electricity price optimization model. The functions, the performance and all links in the electricity consumption information acquisition system are organically combined with the electricity price control, the collection, the meter reading expansion and the proofreading of relevant information are implemented in a standard mode, the times of the complicated links, the circulation links and the like are reduced by using the maximum capacity, the operation of each task link is realized with better quality by designing different modules and integrally planning and strictly arranging, and the improvement of the electricity price control mode is effectively completed in the operation process.

Description

Method and system for calculating and acquiring electricity consumption information with stepped electricity price optimization model and electricity quantity data server

Technical Field

The invention relates to the technical field of power data processing, in particular to a method and a system for calculating and acquiring power consumption information with a step power price optimization model and a power quantity data server.

Background

The step-type electricity price is short for step-type incremental electricity price or step-type progressive electricity price, and means that the electricity consumption of a user is set to be a plurality of step-type sectional or graded pricing calculation costs. The energy efficiency can be improved by increasing the electricity price stepwise for the electricity consumption of residents.

The advantage of ladder price of electricity can serve the demand of vast power consumer, also can realize the good economic benefits of electric power enterprise, under the current condition of using electricity information acquisition system widely, current power consumption information calculation process is comparatively complicated, and the electric power data volume of involving is great, the unable timely electric power data information that obtains, and the calculation of some data is gone on with acquireing in a circulating way, has increased iterative work load.

And to the processing of cascaded electricity price need consider resident's power consumption demand, reasonable setting of resident's charges of electricity, electric power enterprise's power supply cost consideration and power consumption seasonal difference's factor such as, step electricity price needs stepping processing electric quantity data like this, stepping processing electricity price, and classify based on resident's power consumption and enterprise's power consumption, further increased the degree of difficulty of step electricity price, existing mode at present, the precision is perhaps slightly poor in the timely degree of matching that updates sometimes.

Disclosure of Invention

In order to overcome the defects in the prior art, the invention provides a method for calculating and acquiring power consumption information with a step power price optimization model, which comprises the following steps:

step one, configuring a stepped electricity price grading grade model based on a rank and ratio method;

step two, configuring a grading electric quantity processing model;

step three, configuring a grading electricity price processing model;

and step four, processing and displaying the stepped electricity price electric quantity based on the stepped electricity price grading level model, the grading electric quantity processing model and the grading electricity price processing model.

The invention also provides a system with a step electricity price optimization model for calculating and processing electricity consumption information, which comprises: the system comprises a step electricity price data acquisition module, a step electricity quantity data acquisition module, an electricity data transceiving module and an electricity quantity data server;

the step electric quantity data acquisition module is used for acquiring electric quantity data of each power consumer in the distribution room;

the step electricity price data acquisition module is used for receiving the grading electricity price data sent by the electricity data server, processing the step electricity price electricity quantity based on the step electricity price grading grade model, the grading electricity quantity processing model and the grading electricity price processing model, and displaying the electricity price electricity quantity information of the current power consumer;

the electricity data receiving and sending module is used for receiving the IP address set by the electricity data server for each electricity consumer and sending electricity price and electricity quantity information of the current electricity consumer to the electricity data server at each electricity data sending time point;

the electric quantity data server is used for processing the stepped electricity price electric quantity based on the stepped electricity price grading grade model, the grading electric quantity processing model and the grading electricity price processing model for the electricity price and electric quantity information of each electricity consumer.

The electric quantity data server is also used for receiving the electricity price and electric quantity information of the electricity consumers at the time point of receiving the electricity data, and archiving and storing the electricity price and electric quantity information of the corresponding electricity consumers according to the received IP address;

constructing a step electricity price layout diagram of the electricity utilization area, and updating the step electricity price and the step electricity quantity state of the electricity users in the layout diagram in real time;

configuring a step electricity price optimization model operation interface to enable an operator to add power data which are not stored or configured in the system; or modifying or deleting the stored step electricity price and the step electricity quantity state;

the method comprises the steps that a control information command is sent to a power consumer terminal in real time based on a local area network or a wide area network, step electricity price and step electricity quantity state information of the power consumer are obtained, and the obtained step electricity price and step electricity quantity state information of the power consumer are compared with a preset threshold value to obtain current state information;

predicting the electricity utilization trend of the current electricity consumer to form a bar chart or a curve chart for reference use of an operator;

the system is also used for tracking and collecting the step electricity price and the step electricity quantity state of each electricity consumer, realizing data sharing, and forming a step electricity price and step electricity quantity state comparison trend graph and a comparison state graph among the electricity consumers of the same category;

and is also used for trend display of the step electricity price and step electricity quantity state information of each electricity consumer every day, week, month and quarter.

The invention also provides an electric quantity data server for realizing the meter with the step electricity price optimization model and the electricity information acquisition method, which comprises the following steps:

the memory is used for storing a computer program and a meter with a step electricity price optimization model and a power utilization information acquisition method;

and the processor is used for executing the computer program and the meter and electricity utilization information acquisition method with the step electricity price optimization model so as to realize the steps of the meter and electricity utilization information acquisition method with the step electricity price optimization model.

According to the technical scheme, the invention has the following advantages:

the invention relates to a method and a system for calculating electricity consumption information and an electricity consumption data server with a step electricity price optimization model, and the electricity consumption information acquisition method, the system and the electricity quantity data server respectively formulate a grading grade optimization model, a grading electricity quantity optimization model and a grading electricity price optimization model by combining factors such as residential electricity demand, residential electricity charge bearing capacity, power supply cost of power enterprises, seasonal difference of residential electricity consumption and the like.

The invention measures and uses the electricity charge of the electricity consumer based on the grading grade optimization model, the grading electricity quantity and the grading electricity price optimization model. The functions, the performance and all links in the electricity consumption information acquisition system are organically combined with the electricity price control, the collection, the meter reading expansion and the proofreading of relevant information are implemented in a standard mode, the times of the complicated links, the circulation links and the like are reduced by using the maximum capacity, the operation of each task link is realized with better quality by designing different modules and integrally planning and strictly arranging, and the improvement of the electricity price control mode is effectively completed in the operation process.

Drawings

In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.

FIG. 1 is a flow chart of a method for calculating and collecting electricity consumption information with a stepped electricity price optimization model;

FIG. 2 is a schematic diagram of a normalized normal distribution density image and an aggregate density image;

FIG. 3 is a chart of three level classifications by rank and rank comparison;

FIG. 4 is a distribution diagram of electric quantity of residential users;

FIG. 5 is a diagram showing a relationship between a correction parameter and a power consumption increase;

FIG. 6 is a diagram showing the relationship between the average daily power consumption price and the power consumption of the client;

FIG. 7 is a graph of customer demand for electricity versus electricity charge;

FIG. 8 is a graph comparing stepped electricity prices with single electricity prices;

fig. 9 is a system diagram.

Detailed Description

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations or operations have not been shown or described in detail to avoid obscuring aspects of the invention.

The invention provides a meter with a step electricity price optimization model and a power consumption information acquisition method, as shown in figure 1, the method comprises the following steps:

s11, configuring a stepped electricity price grading grade model based on a rank and ratio method;

s12, configuring a grading electric quantity processing model;

s13, configuring a grading electricity price processing model;

and S14, processing and displaying the stepped electricity price electric quantity based on the stepped electricity price grading level model, the grading electric quantity processing model and the grading electricity price processing model.

In order to further illustrate the embodiments of the present invention, specific examples are described below.

1. Rank and ratio method-based grading grade optimization model

1) Standard normal distribution

The content of the rank-sum ratio method is the normal dispersion, as shown in fig. 2, the solid line represents the normal dispersion density curve, and the corresponding function is:

the dotted line represents a normalized normal dispersion sum frequency image, and the corresponding equation is:

table 1 shows the case where the total frequency changes with the self-coefficient in the normal dispersion.

TABLE 1

2) Probability unit calculation

In the normal dispersion, the upper limit values of different total frequencies are correspondingly different, but in the rank-sum ratio method, the corresponding upper limit values under different sample total frequency are frequency units.

Since φ (x) is a monotonically increasing function, the cumulative probability increases with increasing argument, in other words, the argument of the function increases correspondingly with increasing cumulative probability, i.e., φ (x) is also a monotonically increasing function. However, the rank-sum ratio method applies the characteristic and solves the calculation example to sum up the frequency number units. The relative transfer function is:

Y＝φ^-1(x)+5 (3)

there are therefore various probability units of rank and rank ratio under the total frequency according to this equation, as shown in table 2:

TABLE 2

3) Sample distribution test

Because the premise of the rank-sum ratio method is standard normal dispersion, the dispersion condition of the example must be checked before classification, and the method is guaranteed to comply with the normal dispersion. However, the check of the algorithm dispersion is performed by using a method of modeling the correlation formula of the algorithm estimation value sequence { RSR } and the accounting frequency unit sequence { Y }.

Constructing a unary linear regression model:

solving linear parameters according to the information of the arithmetic example of the sequence { RSR } and the sequence { Y } by a least square method, wherein the related formula is as follows:

whether the sample obeys the rule of normal distribution can be judged by checking relevant parameters of a fitting linear regression equation of the sequence { RSR } and the sequence { Y }. The calculation equation for the parameters is as follows:

where n is the sample volume.

When R tends to 1, the sequence { RSR } is in obvious positive relation with the sequence { Y }, which means that the sequence { RSR } follows the rule of normal dispersion, and a rank-sum ratio method can be used for carrying out deeper hierarchical innovation; if R does not tend to 1, an analysis should be performed for the sequence { RSR }, thereby ensuring that the rules of a normal distribution are followed.

4) Sample grading

The rank-sum ratio method is performed according to the separation probability unit correlation region [2, 8], the total frequency of the region is close to 100%, and the total frequency of the samples can be approximately included. Fig. 4 shows three hierarchical classifications of the rank and scale method, where a region [2, 8] is formed into 3 regions of equal length, and then classification points 4 and 6 are present, and then classification regions (— infinity, 4], (4, 6], (6, + ∞) are formed, and each interval is a probability unit hierarchy for solution.

But for the m-class frequency unit classification, the i-th class frequency unit maximum limit

And minimum amount _iUThe following equation is calculated:

when i is 1, there are:

when i is 2, 3, …, m-1, there are:

when i ═ m, there are:

wherein the content of the first and second substances,

to partition nodes.

The classification conclusion of the rank-sum ratio method for classifying probability units according to three to six classes is shown in table 3:

TABLE 3

According to each classification region, a straight line return method is used to solve classification breakpoints corresponding to the number series { RSR }, namely:

wherein the content of the first and second substances,

is the number series { RSR } ith classification breakpoint, Y_i ^pIs the number series { Y } ith classification break point.

5) Optimal staging determination

The rule defined by the optimal number of grades is that the variance of each grade is the same and the variance has the characteristic of being explicit. Therefore, the classification process needs to compare the classification effect of the grades under each classification number through the verification with the same standard deviation, and finally, the proper classification number is determined.

The verification of the ladder stepping is usually performed by X²The check or F-check is performed for identity of variance at each level and for explicit at each level mean.

①χ²Verification

Calculating the X after grading²The value:

χ²＝M/C (12)

n′_i＝n_i-1 (16)

in the formula

Is the variance of the ith class of calculation, n_iIs the number of the ith rank of arithmetic cases, and k is the total number of arithmetic cases in the sequence { RSR }.

To obtain χ²After counting, the distribution table is looked up by V ═ m-1, if the x is under the corresponding confidence region²Ratio χ²With a small margin, the result can be summarized as: the sample variance is the same for each level.

(II) F test

F-value after classification was calculated:

F＝MS_TR/MS_e (17)

in the formula MS_TRAs a mean square of the group and the group, MS_eThe mean square of the interior of the group.

Finding F distribution critical value F according to confidence degree alpha_α(m-1, K-m), if the F value is larger than the critical value, the mean difference of each grade is obvious. Therefore, the gear is the best gear.

(2) Decision index selection of grading times

If classification needs to be carried out according to the difference situation of the domestic electricity utilization capacity of people in each region, the reference standard can directly or indirectly display the difference of the domestic electricity utilization of people. For example, the difference condition of the daily power consumption capacity of people can be obviously displayed by the standards such as the daily power consumption discrete coefficient of people, the standard deviation of the daily power consumption of people, the daily power consumption classification grade ratio of people and the like. In addition, the living power consumption of people is in a positive correlation with the income capacity of people, and the power consumption of high-income people is generally more than that of low-income people; therefore, the difference of daily power consumption capability of people can be taken as a reference through parameters such as urban and rural people pay allocation conditions, a keny coefficient and the like which can represent the difference of people pay.

Because the discrete coefficient comprehensively displays the general ability of people for living power consumption and the range of the power consumption deviation average value of users of various grades, the economic parameters have certain limitation on the performance range aiming at the habit of people for living power consumption. So the discrete parameters of the daily electricity consumption of people are selected as the classification basis for the classification number.

Selecting n characteristic examples of daily power consumption of people, and calculating C.V discrete parameters of daily power consumption of people in different examples_i(i ═ 1,2, …, n). Secondly, the discrete parameters C.V of each region are determined_jIn order from small to large to obtain a more advanced discrete parameter sequence C.V_j(C.V_i<C.V_j+1) And in C.V_jAs RSR_j。

(3) Sample distribution test

For the dispersion case of the sequence { RSR }, the aggregate frequency (j/n) of the increasing order of the RSR values should be displayed in the form of probability. In this case, a linear function of { RSR } with probability units is constructed:

then checking the simulation condition of the linear function, if the judgment parameter tends to 1, judging that the discrete parameter of the daily power consumption of people in the selected area complies with the standard normal dispersion, and grading the discrete parameter by a rank and ratio method. If the distribution of RSR is shifted from normal distribution more, the logarithm lnRSR is taken, and the square is openedRSR^1/2The equal correlation quantity replaces the RSR, thereby letting the samples follow or trend a standard normal distribution.

(4) Optimal staging determination

And carrying out classification according to the scientific classification and the optimal classification in the rank and proportion law to the discrete parameters of the daily power consumption of people. Since the grade electricity rates are generally divided into three to six grades, 4 classification methods are summed. Therefore, the series { RSR } can be divided into 4 categories and checked whether the variances of the various categories are similar and whether the discrete parameter averages of the various categories are significantly different. If the checking can be successful, carrying out three levels of grade electric charge according to the classification condition and facing the region with the lowest discrete parameter of the daily electric consumption of people; aiming at the general lower region of the discrete parameter of the daily power consumption of people, only four levels of grade electric charge are implemented; however, for places with large daily required electricity quantity discrete parameters of load customers, five levels of grade electricity charges are implemented; correspondingly, aiming at the area with the largest discrete parameter of the daily power consumption of people, the grade electric charge of six levels is implemented.

2. Graded electric quantity optimization model

After the number of times of grading of the electricity charges of the step of the life of people is confirmed, then, the optimized setting is carried out for the electricity consumption of various grades. The classified power consumption of various grades should meet the living power demand of people with various living standards as much as possible, if the design of the classified power is unscientific, the conflict of people can be caused in some cases, and the farther the classified power deviates from the actual demand of the living power of people, the higher the conflict is. Therefore, the innovation of the graded power follows the specific requirement of daily power consumption of people, and further, the contradiction of the people to the power regulation is promoted to be minimum.

(1) Response of residents to the stepped electric quantity

If the improvement is carried out for the classified power, the research is carried out for the feedback of the daily power consumption classified power setting of people. According to the analysis of the psychology of people on power consumption, people generally expect that the power grading setting meets the actual requirement of the power consumption of the people. Otherwise, the consumer may not be able to achieve the highest revenue it expects. Hereinafter, using a 3-level daily-level electricity rate system as an example, various expressions of the power setting of the population for various power consumption capacities are studied.

Fig. 4 is a dispersion diagram of the daily power consumption of the customers, showing the number of the customers under various power consumption capabilities. In the figure, the horizontal axis represents daily required electric quantity Q of people, the vertical axis represents the number F of customers of people, and F (Q) represents the total number of customers with daily electric consumption quantity Q; v1 and V2 are graded electric quantity, the first and second stage step electric charge are [0, V1], [ V1+1, V2], and the third stage step electric charge is [ V2+1, + co ].

For the first level load client, as shown in fig. 4, assume that its daily power consumption is Q. The customer of the power consumption level can expect the grading power rate V1 to tend to the own power consumption requirement as much as possible, namely, ensuring the V₁-Q₁And | is minimal. This is because according to the planning principle that the electricity fee is larger in relation to more electricity-consuming power of the increasing grade electricity fee, the less the grade power is, the smaller the corresponding planned electricity fee is. Therefore, if the price setting standard corresponding to the first-level power consumption quantity set as Q1 is smaller than the price setting standard when the classification power is set as K, the power consumption customers of the level can use the lowest power consumption cost to meet the own power consumption requirement, thereby maximizing the benefit.

For the second level load client, as shown in fig. 4, it is assumed that its daily power consumption amount is Q2. The customer of this power consumption level may not expect the grading power V2 to be equal to its own power consumption requirement, but may expect the grading power V1 to meet its power consumption requirement, i.e. | V₁-Q₂| maximally tends towards 0. This is because if the second stage classification power is V2, it still needs to be self | Q₂-V₁The electric quantity of the link is used for paying the electric charge related to the second-level electric quantity; however, if the grading power V1 can cover its power requirement, the customer can pay the first grade of power fee to use the lowest grade of power fee to the maximum extent.

But for the third level load client, its power consumption psychology and the second level client classSimilarly, it is desirable that the classification power amount V2 may correspond to its living power consumption amount Q3, i.e. | V₂-Q₃And | maximally trending to 0, and further paying the lowest electricity price.

According to the above situation, the agreement condition of the first-level load client is determined according to the difference between the power consumption quantity Q1 and the first-level power quantity V1, and the more similar the two aspects, the smaller the number of unsaturations; however, for the second or higher level load client, the satisfaction condition is determined by the difference between the self required power Qi (i is 2, 3) and the lower level grading power Vi-1, and the less difference, the less dissatisfaction condition.

(2) Optimization model

According to the research on the reaction of the load customers to the grading electric quantity, the grading electric quantity of each grade is supposed to be close to the whole electricity utilization requirement of each grade customer to the maximum extent, and the overall discontent condition of the customers is reduced. On the condition of a large number of load customers, the dispersion state of daily power consumption of people can be used as a continuous parameter along with the change of the power consumption, so that an improved objective function of power classification is generated in order to minimize the client's dislike state of classified power:

back side of V'_QIs a graded power that is very noticeable to human customers who consume a quantity Q of power. V 'is provided for a first-level load client with the electricity consumption quantity of Q1'_Q＝V₁(ii) a For the second level load resident with power demand of Q2, there is V'_Q＝V₁(ii) a V 'for the third-level civil customer with the electricity consumption quantity of Q3'_Q＝V₂。

Optimizing gamma (Q-V ') in target equation'_Q) To enlarge the parametric equation, the accompanying classification power V 'is illustrated'_QThe deviation of the daily electricity consumption quantity of people needs to be improved by the Q standard, and people customers aim at the expansion condition of the graded power reaction condition. When r is>1 hour, customer dissatisfaction will increase rapidly with the expansion of the difference. When 0 is present<r<1, the aversion condition will increase slowly with the expansion of the difference. When r is 1, the customer's counterintuitive situation is simultaneously increased linearly; at this time, the overall aversion of the customer with the electricity consumption Q in fig. 6-4 can be indicated by the area of the rectangle ABKQ 1; however, the customer's reaction to the electricity consumption Q1 can be indicated by the area of the rectangle CDQ2V 1.

(3) Optimization result timeliness correction

The optimization of the grading electric quantity model is based on the traditional monthly power consumption information of the electricity consumed by people in life, and the conclusion of the model optimization only represents the previous electricity consumption requirements of the client and cannot completely represent the future life power consumption requirements of the client. However, the electricity charge of the life of the client is already indicated, and the electricity classification of the client can be kept stable for a period of time. Therefore, the conclusion of improvement of the classification power amount needs to be corrected in time efficiency according to the expansion of the life power consumption of the client.

1) Resident life electricity information processing

The expected demand of the future life power consumption of each level of load client is taken as the basis according to the traditional power consumption average value of each level of client. According to the optimization conclusion, the coverage ratio of each grading electric quantity to the life electricity consumption of the client is set as theta₁、θ₂…θ_mThen, the generalized sample of the customer power consumption in the previous N0 years is expressed by theta₁、θ₂…θ_mThe ratio of (a) to (b) is summarized to an average value of power consumptions of the clients at each level in the corresponding year, and further becomes a summarized matrix of a certain classification level and classified according to year, such as the following: q_ijRepresenting the average daily power consumption of the load customers of the ith class in the jth year.

2) Power time series prediction

According to the situation that the living power consumption of each level load client needs to be increased in recent years, prediction is implemented aiming at the power consumption requirement of the client in two to three years later, and a prediction equation of the increasing time series of the living power consumption requirement of the client within the implementation period of the stepped power rate is solved. The following constructs a unitary quadratic function model of the design time series of the consumer life power consumption:

and (3) performing fitting by using a least square method, and solving coefficients ai, bi and ci to further confirm the expansion trend of the general life power consumption of the ith-level client. If the period for implementing the stepped electricity rate is N1 years, the total expansion rate of the daily electricity consumption of the client after N1 years is:

3) graded power correction

After the regression equation is constructed, the grading electric quantity Q of each grade needs to be calculated_i' make corrections to meet the power consumption needs of the load client at this level for a later period of time.

Q'＝ωQ (23)

Wherein, omega is a correction parameter, the access interval is [1, 1+ a ], and the value is determined according to the reasons of the expansion and change situation of the daily power consumption of the client, the regulation period of the electric charge, the daily power requirement characteristic of the client and the like. The omega theory should analyze the expansion of the power consumption of the customers in the relevant level in detail, as shown in fig. 5, the expansion criteria of a are all completed in the N0+ N1 year, however, the value theory of w should be different. For the situation (a >0) in which the increase rate of power consumption amount in customer life increases, a relatively low value is suitably used; conversely, if the rate of increase in the customer's daily power consumption is in the direction of the turndown (a <0), then a relatively high number should be taken.

3. Grading electricity price optimization model

Through carrying out improved planning facing to the grading power, the countering situation of the customer facing to the grading power is reduced to the lowest standard; however, this does not mean that the optimal state of the step electric charge scheme is achieved, and whether the step electric charge scheme is good or bad also needs to be related to whether the planning of each level of electric charge is scientific or not.

(1) User response to tiered price

1) Supply curve

According to the principle of the daily increasing grade electricity fee of the client, the average price of the electricity consumption is accompanied with the change of the electricity consumption as shown in FIG. 6: the average graph of the electric energy consumption creates a turning point at both the classification powers V1, V2, however, the average price of the customer's electric power consumption gradually moves toward the highest-grade electric power rate P3 with the increase of the customer's daily electric power consumption.

The curve in fig. 6 can be regarded as a supply curve of the customer as an individual, and can be regarded as a supply curve of life power consumption of the customer at each level, for example, a second-level load customer whose average price of power consumption changes along the curve AB with an increase in the average power consumption, so that the curve AB can be regarded as a supply curve of the second-level customer. The average price of electricity consumption of each level load customer is as follows:

in the formula, the first step is that,

is the average price of power consumption of the ith level load customers; p_jStep electricity fee of j grade; can be made of

Average power consumption at level j for level i load customers.

2) Demand curve

With the gradual increase of the electricity charges on the stairs, the load customers may reduce the application of power due to the replacement effect. If it is not

And generating a logarithmic function equation of the load client requirement and the general power consumption price for the load client at the ith level:

wherein, theta_i(dQ/Q)/(dP/P) ═ dlnQ/dlnP is the power of the i class power customersA value change parameter. Since the revenue capacity of a heavily power consuming load client is generally stronger than that of a less power consuming load client, it is more burdened with changes in electricity rates and less sensitive to changes in electricity rates, i.e., | θ_i|>|θ_i+1|。

Furthermore, various pricing plans can be highly related to the electricity requirements of the load customers, as shown in FIG. 7 below: s_h、S_lCorrespondingly representing two sets of price planning, namely a large set and a small set. Average power consumption Q of each level of load customers under high pricing plan_hWill be lower than the average Q of the power consumption in the pricing plan_lI.e. having theta_i<0

3) Stepped tariff constraint

There are some differences in the load level of various revenue-capable load customers for electricity charges, and high-revenue customers are relatively insensitive to an increase in electricity charges, while low-revenue customers are sensitive to an increase in electricity charges. Therefore, the grading electric charge management of the step electric charge can comprehensively relate to the burden levels of different electricity utilization level customers for the electric charge, ensure that the payment of the electric charge can not exceed the maximum burden level of each level customer, and ensure that the implementation of the step electric charge can not form great obstacle to normal production.

R_iIs the average revenue, β, of the ith level of loaded customers_iAnd paying the maximum limit of the income proportion for the electric charge which can be borne by the ith level load client.

In other words, the level cost formulation theory should show the difference of the client cost payment accounting for the corresponding payroll ratio of various payroll levels, and further show the important concept of fairness, reasonableness and energy conservation, and the cost payment accounting for the payroll of the client with high payroll capacity should be correspondingly improved according to the original cost standard after optimization. FIG. 8 is a comparison of the rate of customer payment to the corresponding payroll with a change in payroll capacity for the first level of cost when the primary cost criteria are the same as the primary cost criteria. It can be known from the study of the figure that the proportion of the electric power expenditure of each grade of customers to the income of the customers is improved under different conditions after the step electric charge is implemented, and particularly, the electric charge payment proportion of the customers with larger grades is obviously greater than that of the customers with single electric charge mode, and tends to the highest acceptable limit. Under this condition, it is possible for the high-cost customers to raise their attention to power expenditure and manage their own power consumption.

(2) Power supply company response to electricity prices

When the burden capacity of the customer on the electricity charge is not considered, the efficiency of the power supply enterprise is considered by the regulation of the grading electricity charge, and the continuous progress of the operation of the power supply enterprise is guaranteed. Under the condition of not influencing the burden level of a client, the planning of the stepped electricity charge conforms to the requirement of a power supply enterprise for obtaining certain profits to the maximum extent.

1) Average price of selling electricity

For power supply enterprises, the implementation of the step electric charge management ensures that the average price of the electricity sold for the life electricity of the customers can make up for other costs of power grid investment, maintenance, control and the like of the power enterprises aiming at the life electricity consumption of the customers. In addition, in terms of energy utilization fairness, the daily power consumption of the load customers after the grade electricity fee is implemented should reduce the compensation of other industrial power consumption customers for the power consumption of the customers to the maximum extent. Therefore, the average price of electricity sold should satisfy the following equation:

ω_iis the ratio relationship between the ith level load client and the total load client; and c is the power distribution cost of the unit electric quantity of the power distribution enterprise.

2) Profit from selling electricity

Customer preferences for power expenditure are generally different in various pricing models: the higher the price is made, the less power expenditure preferences the customer is burdened with. For the load customers with high electric charge flexibility, the adjustment of the electric charge can lead to the large reduction of the electric consumption. In this situation, even if the power supply enterprise unit sells electric energyThe profit is increased slightly, but the total profit of electricity selling is reduced in a large range due to the reduction of the electricity consumption of the customer life, and further, the loss of the power enterprises is serious. Therefore, in addition to ensuring the profit of power selling of the power supply enterprise unit, the implementation of the stepped power fee also ensures that the total profit of the power supply enterprise on the power selling task of the customer life is not affected. Assuming that the number of all social clients is N, the previous single electricity rate is P0, and the previous average power consumption of the i-th level load client is

Then there are:

simplifying to obtain:

(3) optimization model

The important purpose of implementing the step electricity charge of the load customer is to support the energy saving and the power consumption, reduce the waste of the electric energy and further promote the realization process of energy saving and emission reduction. According to response research of a load client and a power supply enterprise on electric charges, a step electric charge optimization model which aims at minimizing the use of the electric energy per household is constructed by taking the burden level of the client on the electric charges, the unit electricity selling profit of the power supply enterprise and the total benefit as constraints:

based on the above method, the present invention further provides a meter with a stepped electricity price optimization model and an electricity consumption information acquisition and processing system, as shown in fig. 9, including: the system comprises a step electricity price data acquisition module 1, a step electricity quantity data acquisition module 2, an electricity data receiving and sending module 3 and an electricity quantity data server 4;

the step electric quantity data acquisition module 1 is used for acquiring electric quantity data of each power consumer in the distribution area;

the stepped electricity price data acquisition module 2 is used for receiving the stepped electricity price data sent by the electricity data server, processing stepped electricity price electricity quantity based on the stepped electricity price grading grade model, the graded electricity quantity processing model and the graded electricity price processing model, and displaying electricity price electricity quantity information of the current power consumer;

the electricity data receiving and sending module 3 is used for receiving an IP address set by the electricity data server for each electricity consumer and sending electricity price and electricity quantity information of the current electricity consumer to the electricity data server at each electricity data sending time point;

the electricity data server 4 is used for processing the electricity price and electricity quantity information of each electricity consumer based on the stepped electricity price grading level model, the grading electricity quantity processing model and the grading electricity price processing model.

Based on the method and the system, the invention also provides an electric quantity data server for realizing the electricity consumption information acquisition method and the calculation with the step electricity price optimization model, which is characterized by comprising the following steps:

The electricity data server with a stepped electricity price optimization model and accounting for electricity information collection methods is the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein, and can be implemented in electronic hardware, computer software, or a combination of both, and the components and steps of the examples have been generally described in terms of functionality in the foregoing description for clarity of illustrating interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

Through the above description of the embodiments, those skilled in the art will readily understand that the electricity data server with the stepped electricity price optimization model and the electricity information collection method described herein may be implemented by software, or may be implemented by software in combination with necessary hardware. Therefore, the technical solution of the embodiments disclosed in the method for collecting power consumption information according to the meter with the stepped power rate optimization model may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the indexing method according to the embodiments disclosed in the present disclosure.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A meter with a step electricity price optimization model and a power utilization information acquisition method are characterized by comprising the following steps:

step two, configuring a grading electric quantity processing model;

step three, configuring a grading electricity price processing model;

2. The method of claim 1,

the first step further comprises the following steps:

selecting a step electricity price electricity quantity data index of grading times;

configuring a standard normal distribution state of the step electricity price electricity quantity data;

detecting the distribution of the step electricity price and electricity quantity data samples;

calculating a probability unit of the step electricity price and quantity data;

classifying the grade of the step electricity price electric quantity data sample;

and determining the grading of the step electricity price and quantity data.

3. The method of claim 2,

selecting the daily power consumption of n power consumers from the data indexes of the stepped electricity price and electricity quantity with the selected grading times, and calculating the discrete parameter C.V of the daily power consumption of the power consumers_i(i＝1,2,…,n)；

C.V discrete parameters of each power utilization area_jSorting according to the sequence from small to large to obtain a discrete parameter sequence C.V_j(C.V_i<C.V_j+1) And in C.V_jAs RSR_j；

In a standard normal distribution state configured with stepped electricity price electric quantity data, normal dispersion is normalized based on a rank and ratio method, a representative normalized normal dispersion density curve is realized, and the corresponding function is as follows:

in a probability unit for calculating the data of the electricity price and the electricity quantity of the step, based on the fact that phi (x) is a monotonically increasing function, the accumulative probability increases along with the increase of the independent variable, namely, the phi (x) is also the monotonically increasing function;

solving an arithmetic example total frequency number unit based on a rank sum ratio method; the relative transfer function is:

Y＝φ^-1(x)+5 (3)

therefore, the probability units of the rank and ratio method under various total frequencies are obtained according to the formula (3);

in the step electricity price data sample distribution test, on the premise of a rank and ratio method, standard normal dispersion is carried out, and the dispersion condition of an example is checked before classification;

constructing a unary linear regression model:

judging whether the sample obeys the rule of normal distribution or not through relevant parameters of a fitting linear regression equation of the check sequence { RSR } and the sequence { Y }; the calculation equation for the parameters is as follows:

wherein n is the sample volume;

when R tends to 1, the relation that the sequence { RSR } is obviously and positively connected with the sequence { Y } is shown, and the sequence { RSR } follows the rule of normal dispersion, so that deeper hierarchical innovation can be performed by using a rank-sum ratio method;

if R does not tend to 1, performing analysis on the sequence { RSR } to ensure that the rule of normal distribution is observed;

in the step of dividing the grade of the step electricity price electric quantity data sample, finishing according to a separation probability unit related region [2, 8], wherein the total frequency of the region is close to 100 percent and comprises the total frequency of the step electricity price electric quantity data;

forming 3 regions with equal length in the region [2, 8], and forming each classified region (- ∞, 4], (4, 6], (6, + ∞) when the classified points 4 and 6 appear, wherein each interval is the solved probability unit grading;

for m-class frequency unit classification, i-th class frequency unit maximum limit

And minimum amount _iUThe following equation is calculated:

when i is 1, there are:

when i is 2, 3, …, m-1, there are:

when i ═ m, there are:

wherein the content of the first and second substances,

dividing nodes;

classification when classifying probability units according to three to six levels according to equation (8), equation (9), equation (10) rank-sum ratio:

wherein the content of the first and second substances,

is the number series { RSR } ith classification breakpoint, Y_i ^pIs a series { Y } ith classification break point;

in determining the grading of the stepped electricity rate capacity data,

passing through chi²Checking or F checking to check the variance identity of each level and the average value of each level;

①χ²verification

Obtaining the classified χ²The value:

χ²＝M/C (12)

n′_i＝n_i-1 (16)

in the formula

Is the variance of the ith class of calculation, n_iIs the number of the ith rank of arithmetic cases, k is the total number of arithmetic cases for the number series { RSR };

to obtain χ²After counting, the distribution table is looked up by V ═ m-1, if the x is under the corresponding confidence region²Ratio χ²The boundary is small, and the sample variances of all levels are the same;

(II) F test

Obtaining the F value after grading:

F＝MS_TR/MS_e (17)

in the formula MS_TRAs a mean square of the group and the group, MS_eMean square of the interior of the group;

finding F distribution critical value F according to confidence degree alpha_α(m-1, K-m), if the value of F is larger than the critical value, the mean difference of each grade is obvious, and the current grading mode is determined.

4. The method of claim 2,

in the step electricity price electricity quantity data sample distribution, aiming at the dispersion situation of the sequence { RSR }, displaying the total frequency (j/n) of the increasing order of the RSR numerical value in a probability form;

construct a linear function of { RSR } with probability units:

checking the simulation condition of the linear function, if the judgment parameter tends to 1, judging that the discrete parameter of the power consumption of the power consumers in the selected area obeys standard normal dispersion, and grading by a rank and ratio method;

if the distribution deviation normal distribution of the RSR exceeds a preset threshold value, taking logarithm lnRSR and square RSR^1/2The correlation quantity replaces the RSR, such that the step price electricity quantity data samples follow or tend to a standard normal distribution.

5. The method of claim 2,

the second step further comprises:

acquiring the types of all power consumers in a power utilization area, and dividing the grades of the stepped power rates according to the types and the power consumption of the power consumers;

when the number of the users exceeds the preset number, the dispersion state of daily power consumption of the users is used as a continuous parameter along with the change of the number of the power consumption, and an improved objective function of power classification is configured:

V′_Qthe power consumption quantity is Q, and the grading power of the users is obtained; v 'is provided for a first-level load client with the electricity consumption quantity of Q1'_Q＝V₁(ii) a For the second level load resident with power demand of Q2, there is V'_Q＝V₁(ii) a V 'for the third-level civil customer with the electricity consumption quantity of Q3'_Q＝V₂；

Optimizing gamma (Q-V ') in target equation'_Q) To enlarge the parametric equation, the accompanying classification power V 'is illustrated'_QShifting the daily electricity consumption quantity of the electricity consumers to improve the Q standard, and aiming at the expansion condition of the graded power reaction condition of the electricity consumers;

when r is larger than 1, the discontent condition of the power consumer can be rapidly improved along with the expansion of the difference;

when r is more than 0 and less than 1, the countering situation of the power consumer is slowly improved along with the expansion of the difference;

when r is 1, the user's reaction will increase linearly at the same time.

6. The method of claim 5,

the second step further comprises:

processing the resident life electricity information;

the electricity consumers of each level take the expected requirement of the living electricity consumption as the basis according to the traditional electricity consumption average value of the customers of each level;

the coverage ratio of the power consumption of the user is set as theta₁、θ₂…θ_mThen, the generalized samples of the power consumption of the previous N0 years power consumption of the user are respectively expressed by theta₁、θ₂…θ_mThe average value of the power consumption of the power consumers of each grade in the corresponding year is summarized to form a summarizing matrix of a certain classification grade and classification according to the year;

predicting the power utilization time sequence;

according to the situation that the living power consumption needs of the power consumers of all levels are increased, prediction is implemented aiming at the power consumption requirements of two to three years later, and an increase time sequence prediction equation of the power consumption requirements of the power consumers within the implementation period of the stepped power charge is solved;

constructing a unitary quadratic function model of the power consumption design time sequence of the power consumer:

performing fitting by using a least square method, and solving coefficients ai, bi and ci to further confirm the expansion trend of the general life power consumption of the power consumer of the ith grade;

if the period for implementing the stepped electricity rate is N1 years, the total expansion rate of the daily electricity consumption of the client after N1 years is:

correcting graded electric quantity

After a regression equation is constructed, grading electric quantity Q 'of each grade'_iCorrecting to meet the power consumption requirement of the power consumer in the grade within a period of time later;

Q'＝ωQ (23)

wherein, omega is a correction parameter, the access interval is [1, 1+ a ], and the value is determined according to the reasons of the expansion and change condition of the daily power consumption of the power consumer, the regulation period of the power fee, the daily power requirement characteristic of the power consumer and the like.

7. The method of claim 1,

the third step also comprises:

acquiring response information of a power consumer to the grading electricity price;

the average electricity consumption price of each grade of electricity consumers is configured as follows:

in the formula, the first step is that,

is the average price of power consumption of the ith level load customers; p_jStep electricity fee of j grade;

average power consumption at level j for level i load customers;

configuring a demand curve;

if it is not

And generating a logarithmic function equation of the load customer requirement and the general electricity consumption price for the power consumer of the ith grade:

wherein, theta_iThe parameter of the power value change of the i-level power consumer is (dQ/Q)/(dP/P) ═ d ln Q/dlnP; since the revenue capacity of a heavily power consuming load client is generally stronger than that of a less power consuming load client, it is more burdened with changes in electricity rates and less sensitive to changes in electricity rates, i.e., | θ_i|>|θ_i+1|；

Average power consumption Q under high pricing plan based on power consumers of various levels_hLower than average Q of power consumption in pricing plan_lI.e. having theta_i<0；

Configuring a tiered pricing constraint based on equation (26);

R_iis the average revenue, β, of the ith level of loaded customers_iPaying the maximum limit of the income proportion for the electric charge which can be borne by the ith level load client;

according to response information of both a load client and a power supply enterprise to the electric charge, a step electric charge optimization model which aims at minimizing the use of the electric energy per household is constructed by taking the burden level of the client to the electric charge, the unit electricity selling profit of the power supply enterprise and the total benefit as constraints:

8. a meter and power consumption information acquisition and processing system with a stepped electricity price optimization model, comprising: the system comprises a step electricity price data acquisition module, a step electricity quantity data acquisition module, an electricity data transceiving module and an electricity quantity data server;

9. The system of claim 8,

10. An electricity data server for realizing a meter with a step electricity price optimization model and an electricity information acquisition method is characterized by comprising the following steps:

a processor for executing the computer program and the electricity consumption information collection method and the design with a stepped electricity price optimization model to realize the steps of the electricity consumption information collection method and the design with a stepped electricity price optimization model according to any one of claims 1 to 7.