CN110796283A - Demand side active response oriented electric quantity package optimization design method - Google Patents

Demand side active response oriented electric quantity package optimization design method Download PDF

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CN110796283A
CN110796283A CN201910882437.5A CN201910882437A CN110796283A CN 110796283 A CN110796283 A CN 110796283A CN 201910882437 A CN201910882437 A CN 201910882437A CN 110796283 A CN110796283 A CN 110796283A
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林振智
侯佳萱
刘欣怡
余佳莹
李雅婷
李金城
张智
蒋轶澄
杨莉
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Abstract

The invention discloses an electric quantity package optimization design method for demand side active response. The method comprises the following steps: by referring to the mobile tariff package design of the telecommunication industry, an electric quantity package which actively responds towards a demand side is provided; on the basis of comprehensively considering the influence of the electric charge expenditure and the power utilization mode on the user decision, constructing a comprehensive utility model of the electric quantity package selected by the user; constructing a selection behavior model of a user for the electric quantity package based on a plurality of Logit models; constructing a response model of a user for the electric quantity package based on analysis of the electric quantity package on the electric consumption behavior of the user before and after the electric quantity package is implemented; and establishing an optimization model of the power package design with the minimized system peak-valley difference as an optimization target. The demand side response-oriented electric quantity package obtained by optimization by the method can effectively mobilize the initiative and the interactivity of demand side resources, improves the economic operation level and the reliability of a power grid, and has good economy and practical application value.

Description

Demand side active response oriented electric quantity package optimization design method
Technical Field
The invention relates to the technical field of demand response, in particular to an electric quantity package optimization design method for demand side active response.
Background
In recent years, with the adjustment of industrial structures and the increasing of the living standard of people, the problem of insufficient power supply during peak hours is increasingly prominent. The large-scale grid connection of intermittent renewable energy and the large-scale access of electric vehicles mean that the energy structure of China is changed to the direction of greenization, cleanness and low carbon, and simultaneously, higher requirements are provided for the operation and management of a power grid. Seasonal and periodic shortage of power supply leads to greater and greater supply-demand contradiction, and even can cause serious threat to safe and stable operation of a power grid. The introduction of demand response is an important way for solving the problems, the problem of unbalanced power supply and demand is solved by transferring resources on the demand side, the economic operation efficiency of a power grid is improved, and the reasonable allocation of the resources is promoted. The development of the power demand response work is not slow, but the national market environment and market mechanism are not perfect, the technical level is still to be improved, and the key factor for restricting the implementation of the demand response project is realized. Aiming at the characteristics that the demand response technology in China is not mature enough, the power market openness degree is not high, the power pricing is administrative, and the change of marginal cost cannot be fully reflected, the invention provides a demand side active response strategy, namely an electric quantity package, which is suitable for the current market mechanism and technical level in China.
The concept of package is widely applied in the telecommunication, insurance and other industries, and the price package is a combined marketing mode for meeting the market demand and aiming at obtaining the best economic benefit, namely, various services are combined to meet the requirements of target customer groups of different consumption levels, so that the user obtains the price discount and the profit capacity of enterprises is enhanced. A package is essentially a differentiated marketing approach based on user groups. The method is applied to the power industry, and the power package is a diversified power charge pricing mode which is provided by power vendors or power grid companies in order to meet different requirements and characteristics of users. The foreign electricity selling market is mature and fully competitive, and users have the right to freely select power suppliers and power packages. In order to maintain user stickiness, various vendors have established distinctive (residential) power packages, classified into Standard packages (Standard plates) and Market retail packages (Market detail offers) according to purchase forms, classified into Fixed rate packages (Fixed rates) and Variable rate packages (Variable rate plates) according to rates within a contract period, classified into uniform rate packages (single rate packages) and step rate packages (Block availability rates) according to power rate structures, classified into Fixed rate packages (flat rate prices), peak-valley rate packages (off-peak) and time-of-use rates (time-of-use), classified into Direct account payments (Direct bits), Standard credit payments (Standard credits), prepaid-measures (Pre-pay-meters), and combined into individual packages according to payment forms, and some preferential terms are attached, and the user can obtain corresponding preferential benefits when meeting the requirements of the terms in the contract period. In addition, a green Electric power package (green Electric task), an Electric Vehicle power package (Electric Vehicle Price Plan), a direct-control load power package, an online package (onlinetelecom Plan), an Electric co-purchase package (dual fuel Plan), and the like are also provided. A large number of electricity selling companies emerge in China since a new round of power system reform, under the background of medium-long term transaction, the business of the current electricity selling companies mainly faces to large-scale industrial users, and the current common power package types are as follows: the system comprises a fixed price difference mode, a preferential division mode, an interval charging mode, a combination of deviation assessment values and division ratios, a combination of long-term uniform price and centralized bidding, a bottom-preserving package, a coal-electricity linkage mode and the like. The electric power package of the resident users is firstly released in China by Yunnan province in China, and the excess electric power consumption of the Yunnan province is promoted, but the coverage is not wide only for the users with annual electric power consumption exceeding 4000 kilowatt hours, and only occupies about 5 percent of the total number of the users.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an electric quantity package optimization design method for active response of a demand side.
The technical scheme adopted by the invention is as follows:
a demand side active response oriented electric quantity package optimization design method comprises the following steps:
s1: by referring to the mobile tariff package design of the telecommunication industry, an electric quantity package which actively responds towards a demand side is provided;
s2: on the basis of comprehensively considering the influence of the electric charge expenditure and the power utilization mode on the user decision, constructing a comprehensive utility model of the electric quantity package selected by the user;
s3: constructing a selection behavior model of a user for the electric quantity package based on a plurality of Logit models;
s4: constructing a response model of a user for the electric quantity package based on analysis of the electric quantity package on the electric consumption behavior of the user before and after the electric quantity package is implemented;
s5: and establishing an optimization model of the power package design with the minimized system peak-valley difference as an optimization target.
In the above technical solution, as an optimal choice, in S1, an electric quantity package for demand side active response is proposed by referring to a mobile tariff package design in the telecommunication industry, and the specific implementation method is as follows:
the development mode of the demand response is closely related to the market environment, the implementation objects, the market means and the execution modes of the demand response are different under different market environments, and the demand response development in foreign countries cannot be a hard set despite of rich experience. Aiming at the characteristics that the demand response technology in China is not mature enough, the power market is not high in openness degree, power pricing is administrative, and the change of marginal cost cannot be fully reflected, the power package with active response on the demand side is provided as a novel demand response strategy. The package of electrical quantities may be considered to be a price-based demand response or an incentive-based demand response.
The electric quantity package is used for reference to the design of the mobile tariff package, and certain peak electric quantity and valley electric quantity are packaged and sold to users by utilizing the psychology of saving the electricity consumption of power users. Similar to the time of use electricity price, the unit electricity cost is high during peak hours and low during valley hours. On the basis of a typical load curve of a user, a reward and punishment mechanism is established: under the condition of reducing the power consumption in peak periods and increasing the power consumption in valley periods, the user can purchase more power at the same cost to encourage the user to transfer part of the load in peak periods to the valley periods, so that the effect of peak clipping and valley filling is achieved, the power generation cost is saved, and the resource utilization rate is improved. Meanwhile, compared with the original charging standard, the cost paid by the user for the unit electric quantity is reduced, namely, certain economic benefit is generated for the user when the electric quantity package is implemented.
The method comprises the steps of dividing an electric quantity package into J grades, and for the package J, the basic charge to be paid by a user is Cj. Each grade of the electric quantity package comprises two basic attributes: peak period power and valley period power. Each basic attribute has an attribute given amount, namely the free-to-use electric quantity in the package, and the peak time period electric quantity is QpjThe electric quantity in the valley period is Qvj. Package j contains an amount of electricity Qj＝Qpj+Qvj. When the electricity consumption of the user is in the range, the charging is not carried out on the basis of paying the basic charge. In addition, in the initial stage of the push of the electricity package, in order to encourage the user to select the package, the excess electricity consumption of the user is not punished, namely when the electricity consumption of the user exceeds the attribute donation amount contained in the package, the user charges according to the current peak-valley electricity price mechanism.
The design problem of the electric quantity package facing the response of the demand side is that the optimal electric quantity package scheme is designed in the angle of a power supply company on the basis of the current peak-valley electricity price and with the purposes of peak clipping, valley filling and load curve shape improvement on the premise of giving the package grading number, namely the basic charge and the attribute donation quantity of the basic attribute of the electric quantity package are determined. The potential of peak clipping and valley filling is excavated by guiding users to change the electricity utilization mode so as to reduce peak load, reduce electric power gaps, improve valley load and reduce night air abandoning amount.
Preferably, in S2, on the basis of comprehensively considering the influence of the electricity cost and the electricity usage mode on the user decision, a comprehensive utility model for selecting the electricity package by the user is constructed, and the specific implementation method is as follows:
each package profile must have some utility to the user, who has the incentive to select the package. On one hand, when the package contains less peak time period electric quantity and more valley time period electric quantity, the basic charge of the package is relatively lower, and the utility of the package selected by the user is larger; on the other hand, when the peak period electricity is too little, the valley period electricity is too much, and the electricity consumption habit of the user is greatly influenced, even the user cannot carry out normal life and production activities, and the effectiveness of the user for selecting a package is reduced. Therefore, the utility of the package on the user is measured from the two aspects of the utility of the electricity expense and the utility of the electricity utilization mode. The utility of the electric charge expenditure is used for measuring the utility brought to the user by the reduction of the electric charge expenditure, and the utility of the power utilization mode is used for measuring the utility of the change of the power utilization mode of the user.
In general, the power demand of the user is relatively large in the peak period, and the power demand is small in the valley period. The electric quantity package increases the electric quantity in the valley period while reducing the electric quantity in the peak period, so that the electric charge expenditure of the user is reduced on the premise of meeting the total power consumption requirement of the user, and the electric quantity package is favorable for the user, thereby generating the effect of reducing the electric charge expenditure for the user. The utility of the user electricity fee expenditure is specifically expressed as:
Figure BDA0002206277820000041
in the formula: thetaijShowing the electricity fee expenditure utility of the ith package selected by the ith user; ci0The electricity fee expenditure of the ith class user before the electricity package is implemented; cijAfter the electricity quantity package is executed, the electricity fee expenditure of the jth package is selected by the ith class user.
The monthly peak time interval electric quantity of the ith class of users before the electric quantity package is implemented is Qpi0The electric quantity in the valley period is Qvi0(ii) a The power quantity in the monthly peak time interval of the ith class of users who select the jth package after the power quantity package is implemented is QpijThe electric quantity in the valley period is Qvij. Then there are:
Ci0＝Qpi0×Pp+Qvi0×Pv
Cij＝Cj+|Qpij-Qpj|+×Pp+|Qvij-Qvj|+×Pv
in the formula: | x | non grid+When the value of x is larger than zero, the value of the expression is x; when the value of x is zero or less, the value of the expression is zero.
Before the package electricity price is implemented, the user arranges the electricity consumption according to the production life style of the user; after the electric quantity package is carried out, the user responds to change the own electricity consumption habit to obtain certain economic benefit. The utility of the user selecting the package can be influenced by the change of the power utilization mode, and the utility of the power utilization mode of the user is expressed as follows:
εij＝-(|Qpij-Qpi0|+|Qvij-Qvi0|)
in the formula: epsilonijAnd showing the power utilization mode utility of the ith type of user for selecting the jth package. When the electricity consumption of the user in each time period is equal to the electricity consumption before the electricity package is carried out, the user arranges the production and living electricity consumption in the most comfortable way, and the maximum effectiveness is 0; when the electricity consumption of each time interval changes, inconvenience is brought to users, and the effectiveness of the users is negative; the larger the change of the power consumption in each period is, the smaller the utility of the power utilization mode of the user is.
Comprehensively considering two aspects of the utility of the power utilization mode of the user and the utility of the expenditure of the electric charge, the utility package grading of the package grading to the user is as follows:
Vij＝λ1θij+λ2εij
λ1+λ2＝1
in the formula: vijIndicates the utility of the ith class of users to select the jth package, where1Is the weight value of the utility of the user's electric charge expenditure, lambda2The power utilization method is a weight value of the utility of the power utilization mode of the user. Lambda [ alpha ]1、λ2Different numerical values can be set for different user groups so as to reflect different attention degrees of users to the electricity utilization mode and the electricity fee expenditure.
Preferably, in S3, a selection behavior model of the user for the electric quantity package is constructed based on multiple Logit models, and the specific implementation method is as follows:
in the design process of the electric quantity package, analyzing the selection decision behavior of the user on the package is a crucial link. A model is selected based on the dispersion of the utility maximization theory. The method is suitable for analyzing the selection behaviors of decision makers and is widely applied to the aspects of traffic demand problems, education and occupation selection, consumer commodity demands and the like. The invention adopts the MNL model which is most widely applied in the discrete selection model to predict the probability that a user selects a certain electric quantity package. According to the MNL model, the probability of a user selecting a package can be determined by its utility. Thus, the probability of a fully rational typical user i selecting package j is:
Figure BDA0002206277820000051
in the formula: pijRepresenting the probability of the ith class of users selecting the jth package. Mu is a proportional parameter, when the numerical value is larger, the model is close to a deterministic selection rule, namely, the user only selects the package with the maximum effectiveness; the model approximates a uniform distribution when its value is near zero. The value of μ can be reasonably determined by market research and analysis.
Preferably, in S4, based on analysis of power consumption behavior of the user before and after the power package is implemented, a response model of the user for the power package is constructed, and the specific implementation method is as follows:
after a user selects a certain graded electric quantity package, the electric consumption behavior of the user is restricted by the grade of the package. After the user selects the power package, the total demand of power consumption is not changed, only the power consumption time is shifted, that is, the user is constrained by the power package, and the power consumption in part of peak time period is shifted to the off-peak time period for use, but the total power consumption is not changed. After the user selects the package, the free electric quantity in the package is preferentially used. If the electricity consumption demand of the user is less than the package electricity quantity, the user follows the package setting to use the electricity quantity; when the electricity demand of the user is larger than the package electricity quantity, the more parts are used according to the original electricity consumption habit of the user, namely, the more parts are used in the peak time period and are still used in the peak time period, and the more parts are also used in the valley time period. With Qpi0And Qvi0Respectively representing the original peak time interval and valley time interval electricity consumption of the ith class of users, and then the total electricity consumption before the meal is completed is Qi0＝Qpi0+Qvi0. With QpijAnd QvijRespectively representing the peak time interval electricity consumption and the valley time interval after the ith class of users select the electricity package j, and then the total electricity consumption is Qi＝Qpij+Qvij。
Based on the above analysis, the monthly peak time period electric quantity Q of all the power consumers can be obtainedpElectric quantity Q in valley periodvComprises the following steps:
Figure BDA0002206277820000061
Figure BDA0002206277820000062
in the formula: i represents the number of user categories; siIndicating the number of class i users.
Preferably, in S5, an optimization model of the power package design with the minimized system peak-to-valley difference as an optimization target is established, and the optimization model is solved, and the specific implementation method is as follows:
step 1: constructing an objective function of electric quantity package optimization design:
the purpose of the electric quantity package is to load off peak, guide the user to consume less electricity in the peak period and consume more electricity in the valley period, flatten the load curve and increase the load rate, so the optimization model of the electric quantity package design facing the active response of the demand side is shown as the following formula, and the decision variable is the basic charge C of each package in gradesjIncluding high peak time period electric quantity QpjAnd off-peak time electric quantity Qvj。
Figure BDA0002206277820000063
In the formula: p (t) is the load at the t hour on the daily load curve,
Figure BDA0002206277820000064
represents the highest load on the daily load curve,
Figure BDA0002206277820000065
represents the minimum load on the daily load curve,
Figure BDA0002206277820000066
represents the peak-to-valley difference of the daily load curve.
Step 2: constraint condition for constructing electric quantity package optimization design
(1) Revenue constraint for power grid company to implement power package
From the perspective of the grid company, the grid company must have a profit after the power package is executed, at least cannot lose the profit, otherwise the grid company has no power to execute the power package. On one hand, after the electric quantity package is carried out, the electric charge income of a power grid company is reduced to:
Figure BDA0002206277820000071
on the other hand, after the power grid company realizes peak clipping and valley filling, the power supply cost can be reduced, and the reduced cost is the benefit, including the avoidable capacity benefit, the avoidable electric quantity benefit and the system reliability benefit.
The avoidable capacity gain refers to the average investment cost of power supply equipment such as transformers and power transmission lines, which can be reduced by a power grid company due to the reduction of the highest peak load. Can be expressed as:
B1＝GΔV
Figure BDA0002206277820000072
in the formula: b is1In order to avoid capacity benefit, Δ V is avoidable capacity, and G is average unit cost of power supply equipment such as power grid company transformers, transmission lines, and the like. Delta PiThe peak load value of the ith user reduced in the peak period is shown, I is the total number of users participating in peak clipping and valley filling, sigma is a user simultaneous coefficient, lambda is a spare capacity coefficient of the system, and α is a distribution loss coefficient of the power grid.
The avoidable electricity yield means that the power grid company reduces the cost saved by purchasing peak electricity. Can be expressed as:
B2＝ρΔQ
ρ＝fpg-fvg
in the formula: ρ is the difference between the average on-line electricity price at the peak time and the average on-line electricity price at the valley time, and Δ Q is the amount of electricity transferred from the peak time to the valley time. f. ofpgAverage on-line electricity price at peak time fvgThe average on-line electricity price in the valley period.
The system reliability gains mean that after a power grid company implements an electric quantity package project, the power failure probability is reduced, the system power supply reliability is improved, and the cost for purchasing rotary standby is reduced. Can be expressed as:
B3＝ΔQ×LOLP×(VOLL-SMP)
in the formula: Δ Q is the peak power that can be avoided, lopp is the probability of power system load loss before the power package is implemented, the unit is "day/year", VOLL represents the value of load loss, SMP is the average on-line electricity price.
The avoidable cost of the grid company after the electric quantity package is implemented is as follows:
B＝B1+B2+B3
based on the cost benefit analysis of the above electric quantity package, to ensure that the power grid company is not damaged, the constraint condition can be expressed as:
B≥ΔC
(2) peak and valley time peak load constraints
The purpose of the design optimization of the electric quantity package is to reduce the peak-valley difference, increase the load rate and flatten the daily load curve. When the optimization solution is performed according to the objective function, the situation that the user excessively responds to the package electricity price is avoided: the load that shifts to the low ebb time in peak period is too much for new millet load has appeared in original peak period, new peak load has appeared in original millet period, has the original intention of carrying out the electric quantity package. To prevent the user from over-reacting, the constraint can be expressed as:
max Pp(t)＞max Pv(t)
in the formula: max Pp(t) represents the maximum load during peak hours, max Pv(t) represents the highest load during the valley period.
(3) User utility value constraints
When targeted package grading is designed for users in various markets, the high-grade user is prevented from selecting low-grade electric quantity packages, namely, the user with high electric quantity selects package grading giving low electric quantity, and because the electric quantity in the original peak time period and the electric quantity in the valley time period are higher than the electric quantity given by package grading, the user cannot change the electric consumption behavior of the user, so that the user does not contribute to the goal of peak clipping and valley filling, but gains a profit because the low-grade package is selected. The constraints are set as follows:
Vii＞Vij(j≠i) (i＝1,2,...,I；j＝1,2,...,J)
in the formula: viiSelecting a utility value, V, for an ith package profile designed for a user of an ith marketijSelecting the utility of the jth package ranking for users in the ith market, the constraint ensures that the utility value is the maximum when the user selects its corresponding package ranking. On the premise of user reasonability, the user selects effectsThe probability of grading by the package with the maximum value is maximum, so that the phenomenon that a high-grade user selects a low-grade package to obtain a profit in a plain manner is prevented.
The technical scheme provided by the invention has the beneficial effects that:
the demand response-oriented electric quantity package provided by the invention can effectively mobilize the initiative and the interactivity of demand side resources, improves the economic operation level and the reliability of a power grid, and has good economy and practical application value; the user selection decision model based on package effectiveness constructed by the invention is helpful for package makers to predict the selection behavior of users on packages and estimate the market share of each package, and the electric quantity package optimization design model facing the active response of the demand side can assist the decision makers to design the electric quantity packages.
Drawings
FIG. 1 is a schematic overall flow diagram of the present invention.
Fig. 2 is a system load curve before and after a power package is performed.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent.
The invention relates to an electric quantity package optimal design method facing active response of a demand side, which is implemented by the flow shown in figure 1 and specifically comprises the following steps:
s1: by referring to the design of a mobile tariff package in the telecommunication industry, an electric quantity package for active response of a demand side is provided, and the specific implementation method is as follows: the method comprises the steps of dividing an electric quantity package into J grades, and for the package J, the basic charge to be paid by a user is Cj(ii) a Each grade of the electric quantity package comprises two basic attributes: peak time period electric quantity and valley time period electric quantity, each basic attribute has an attribute present quantity, namely the electric quantity which can be used for free under the package, and the peak time period electric quantity is QpjThe electric quantity in the valley period is Qvj(ii) a Package j contains an amount of electricity Qj＝Qpj+QvjWhen the electricity consumption of the user is in the range, the charging is not carried out on the basis of paying the basic charge, in addition, in order to encourage the user to select the package in the initial stage of the push of the electricity package, the penalty is not carried out on the excess electricity consumption, namely when the electricity consumption of the user exceeds the attribute donation quantity contained in the package, the charging is carried out according to the current peak-valley electricity price mechanism.
Table 1 basic structure of electric quantity package
S2: on the basis of comprehensively considering the influence of the electricity expense and the electricity utilization mode on the decision of the user, a comprehensive utility model for selecting the electric quantity package by the user is constructed, and the specific implementation method is as follows:
each package profile must have some utility to the user, who has the incentive to select the package. On one hand, when the package contains less peak time period electric quantity and more valley time period electric quantity, the basic charge of the package is relatively lower, and the utility of the package selected by the user is larger; on the other hand, when the peak period electricity is too little, the valley period electricity is too much, and the electricity consumption habit of the user is greatly influenced, even the user cannot carry out normal life and production activities, and the effectiveness of the user for selecting a package is reduced. Therefore, the utility of the package on the user is measured from the two aspects of the utility of the electricity expense and the utility of the electricity utilization mode. The utility of the electric charge expenditure is used for measuring the utility brought to the user by the reduction of the electric charge expenditure, and the utility of the power utilization mode is used for measuring the utility of the change of the power utilization mode of the user.
In general, the power demand of the user is relatively large in the peak period, and the power demand is small in the valley period. The electric quantity package increases the electric quantity in the valley period while reducing the electric quantity in the peak period, so that the electric charge expenditure of the user is reduced on the premise of meeting the total power consumption requirement of the user, and the electric quantity package is favorable for the user, thereby generating the effect of reducing the electric charge expenditure for the user. The utility of the user electricity fee expenditure is specifically expressed as:
Figure BDA0002206277820000101
in the formula: thetaijShowing the electricity fee expenditure utility of the ith package selected by the ith user; ci0The electricity fee expenditure of the ith class user before the electricity package is implemented; cijAfter the electricity quantity package is executed, the electricity fee expenditure of the jth package is selected by the ith class user.
The monthly peak time interval electric quantity of the ith class of users before the electric quantity package is implemented is Qpi0The electric quantity in the valley period is Qvi0(ii) a The power quantity in the monthly peak time interval of the ith class of users who select the jth package after the power quantity package is implemented is QpijThe electric quantity in the valley period is Qvij. Then there are:
Ci0＝Qpi0×Pp+Qvi0×Pv
Cij＝Cj+|Qpij-Qpj|+×Pp+|Qvij-Qvj|+×Pv
in the formula: | x | non grid+When the value of x is larger than zero, the value of the expression is x; when the value of x is zero or less, the value of the expression is zero.
Before the package electricity price is implemented, the user arranges the electricity consumption according to the production life style of the user; after the electric quantity package is carried out, the user responds to change the own electricity consumption habit to obtain certain economic benefit. The utility of the user selecting the package can be influenced by the change of the power utilization mode, and the utility of the power utilization mode of the user is expressed as follows:
εij＝-(|Qpij-Qpi0|+|Qvij-Qvi0|)
in the formula: epsilonijAnd showing the power utilization mode utility of the ith type of user for selecting the jth package. When the electricity consumption of the user in each time period is equal to the electricity consumption before the electricity package is carried out, the user arranges the production and living electricity consumption in the most comfortable way, and the maximum effectiveness is 0; when the electricity consumption of each time interval changes, inconvenience is brought to usersThe utility is negative; the larger the change of the power consumption in each period is, the smaller the utility of the power utilization mode of the user is.
Comprehensively considering two aspects of the utility of the power utilization mode of the user and the utility of the expenditure of the electric charge, the utility package grading of the package grading to the user is as follows:
Vij＝λ1θij+λ2εij
λ1+λ2＝1
in the formula: vijIndicates the utility of the ith class of users to select the jth package, where1Is the weight value of the utility of the user's electric charge expenditure, lambda2The power utilization method is a weight value of the utility of the power utilization mode of the user. Lambda [ alpha ]1、λ2Different numerical values can be set for different user groups so as to reflect different attention degrees of users to the electricity utilization mode and the electricity fee expenditure.
S3: a selection behavior model of a user for an electric quantity package is constructed based on a plurality of Logit models, and the specific implementation method comprises the following steps:
in the design process of the electric quantity package, analyzing the selection decision behavior of the user on the package is a crucial link. A model is selected based on the dispersion of the utility maximization theory. The method is suitable for analyzing the selection behaviors of decision makers and is widely applied to the aspects of traffic demand problems, education and occupation selection, consumer commodity demands and the like. The invention adopts the MNL model which is most widely applied in the discrete selection model to predict the probability that a user selects a certain electric quantity package. According to the MNL model, the probability of a user selecting a package can be determined by its utility. Thus, the probability of a fully rational typical user i selecting package j is:
Figure BDA0002206277820000111
in the formula: pijRepresenting the probability of the ith class of users selecting the jth package. Mu is a proportional parameter, when the numerical value is larger, the model is close to a deterministic selection rule, namely, the user only selects the package with the maximum effectiveness; the model approximates a uniform distribution when its value is near zero. The value of μ can be expressed byReasonably determined by market research and analysis.
S4: based on analysis of power consumption behaviors of a user before and after implementation of the electric quantity package, a response model of the user to the electric quantity package is constructed, and the specific implementation method comprises the following steps:
after a user selects a certain graded electric quantity package, the electric consumption behavior of the user is restricted by the grade of the package. After the user selects the power package, the total demand of power consumption is not changed, only the power consumption time is shifted, that is, the user is constrained by the power package, and the power consumption in part of peak time period is shifted to the off-peak time period for use, but the total power consumption is not changed. After the user selects the package, the free electric quantity in the package is preferentially used. If the electricity consumption demand of the user is less than the package electricity quantity, the user follows the package setting to use the electricity quantity; when the electricity demand of the user is larger than the package electricity quantity, the more parts are used according to the original electricity consumption habit of the user, namely, the more parts are used in the peak time period and are still used in the peak time period, and the more parts are also used in the valley time period. With Qpi0And Qvi0Respectively representing the original peak time interval and valley time interval electricity consumption of the ith class of users, and then the total electricity consumption before the meal is completed is Qi0＝Qpi0+Qvi0. With QpijAnd QvijRespectively representing the peak time interval electricity consumption and the valley time interval after the ith class of users select the electricity package j, and then the total electricity consumption is Qi＝Qpij+Qvij. The change of the power consumption of the user i after selecting the package j is shown in table 2.
TABLE 2 Power consumption behavior Change after user selects a set meal
Based on the above analysis, the monthly peak time period electric quantity Q of all the power consumers can be obtainedpElectric quantity Q in valley periodvComprises the following steps:
Figure BDA0002206277820000123
in the formula: i represents the number of user categories; siIndicating the number of class i users.
S5: establishing an optimization model of the power package design with the minimized system peak-valley difference as an optimization target, and solving the optimization model, wherein the specific implementation method comprises the following steps:
step 1: constructing an objective function of electric quantity package optimization design:
the purpose of the electric quantity package is to load off peak, guide the user to consume less electricity in the peak period and consume more electricity in the valley period, flatten the load curve and increase the load rate, so the optimization model of the electric quantity package design facing the active response of the demand side is shown as the following formula, and the decision variable is the basic charge C of each package in gradesjIncluding high peak time period electric quantity QpjAnd off-peak time electric quantity Qvj。
In the formula: p (t) is the load at the t hour on the daily load curve,
Figure BDA0002206277820000132
represents the highest load on the daily load curve,
Figure BDA0002206277820000133
represents the minimum load on the daily load curve,
Figure BDA0002206277820000134
represents the peak-to-valley difference of the daily load curve.
Step 2: constraint condition for constructing electric quantity package optimization design
(1) Revenue constraint for power grid company to implement power package
From the perspective of the grid company, the grid company must have a profit after the power package is executed, at least cannot lose the profit, otherwise the grid company has no power to execute the power package. On one hand, after the electric quantity package is carried out, the electric charge income of a power grid company is reduced to:
Figure BDA0002206277820000135
on the other hand, after the power grid company realizes peak clipping and valley filling, the power supply cost can be reduced, and the reduced cost is the benefit, including the avoidable capacity benefit, the avoidable electric quantity benefit and the system reliability benefit.
The avoidable capacity gain refers to the average investment cost of power supply equipment such as transformers and power transmission lines, which can be reduced by a power grid company due to the reduction of the highest peak load. Can be expressed as:
B1＝GΔV
Figure BDA0002206277820000136
in the formula: b is1In order to avoid capacity benefit, Δ V is avoidable capacity, and G is average unit cost of power supply equipment such as power grid company transformers, transmission lines, and the like. Delta PiThe peak load value of the ith user reduced in the peak period is shown, I is the total number of users participating in peak clipping and valley filling, sigma is a user simultaneous coefficient, lambda is a spare capacity coefficient of the system, and α is a distribution loss coefficient of the power grid.
The avoidable electricity yield means that the power grid company reduces the cost saved by purchasing peak electricity. Can be expressed as:
B2＝ρΔQ
ρ＝fpg-fvg
in the formula: ρ is the difference between the average on-line electricity price at the peak time and the average on-line electricity price at the valley time, and Δ Q is the amount of electricity transferred from the peak time to the valley time. f. ofpgAverage on-line electricity price at peak time fvgThe average on-line electricity price in the valley period.
The system reliability gains mean that after a power grid company implements an electric quantity package project, the power failure probability is reduced, the system power supply reliability is improved, and the cost for purchasing rotary standby is reduced. Can be expressed as:
B3＝ΔQ×LOLP×(VOLL-SMP)
in the formula: Δ Q is the peak power that can be avoided, lopp is the probability of power system load loss before the power package is implemented, the unit is "day/year", VOLL represents the value of load loss, SMP is the average on-line electricity price.
The avoidable cost of the grid company after the electric quantity package is implemented is as follows:
B＝B1+B2+B3
based on the cost benefit analysis of the above electric quantity package, to ensure that the power grid company is not damaged, the constraint condition can be expressed as:
B≥ΔC
(2) peak and valley time peak load constraints
The purpose of the design optimization of the electric quantity package is to reduce the peak-valley difference, increase the load rate and flatten the daily load curve. When the optimization solution is performed according to the objective function, the situation that the user excessively responds to the package electricity price is avoided: the load that shifts to the low ebb time in peak period is too much for new millet load has appeared in original peak period, new peak load has appeared in original millet period, has the original intention of carrying out the electric quantity package. To prevent the user from over-reacting, the constraint can be expressed as:
max Pp(t)＞max Pv(t)
in the formula: max Pp(t) represents the maximum load during peak hours, max Pv(t) represents the highest load during the valley period.
(3) User utility value constraints
When targeted package grading is designed for users in various markets, the high-grade user is prevented from selecting low-grade electric quantity packages, namely, the user with high electric quantity selects package grading giving low electric quantity, and because the electric quantity in the original peak time period and the electric quantity in the valley time period are higher than the electric quantity given by package grading, the user cannot change the electric consumption behavior of the user, so that the user does not contribute to the goal of peak clipping and valley filling, but gains a profit because the low-grade package is selected. The constraints are set as follows:
Vii＞Vij(j≠i) (i＝1,2,...,I；j＝1,2,...,J)
in the formula: viiSelecting a utility value, V, for an ith package profile designed for a user of an ith marketijSelecting the utility of the jth package ranking for users in the ith market, the constraint ensures that the utility value is the maximum when the user selects its corresponding package ranking. On the premise that the user is rational, the probability that the user selects the package with the maximum utility value for grading is maximum, and the phenomenon that the high-grade user selects the low-grade package to make a profit is avoided.
The following description is given in conjunction with the examples
The invention takes 544 users in a certain comprehensive area of a certain city in China as research objects. The local peak time is 7:00-22:00, the valley time is 22: 00-the next day is 7:00, and the peak time is the electricity price fp0.96 yuan/kilowatt hour, and the electricity price in the valley period is fv0.43 yuan/kwh. The utility coefficient of the power charge payment is lambda10.5, and the utility coefficient of electricity is lambda2The scale parameter μ in the MNL model is set to 5, 0.5. And clustering daily load curves of the users by adopting a K-means algorithm to extract typical load patterns of the users, subdividing the market and providing references for a power grid company to scientifically and reasonably formulate packages for different types of users. The clustering results are shown in table 3.
TABLE 3 clustering results of user daily load curves
Figure BDA0002206277820000151
The optimization model belongs to a complex nonlinear programming problem with constraints, is difficult to solve by a traditional analytic method, and adopts a genetic algorithm for optimization solution in the research of the invention because the genetic algorithm has the characteristics of global property, robustness and the like. The optimal power package scheme obtained by the user groups of six different types is shown in table 4. Compared with the current peak-valley electricity price in the market, the package grading discount coefficient is changed between 0.85 and 0.90, and a certain preferential discount is given to the user, so that the utility value of selecting the corresponding package by the user is positive, and the user has the power of selecting the electric quantity package. Meanwhile, the larger the total electric quantity demand is, the more obvious the peak clipping and valley filling effect is, the larger the target contribution to peak clipping and valley filling is, so that the higher the grade is, the larger the discount strength is.
TABLE 4 optimal solution for package design model
Figure BDA0002206277820000161
Figure 2 shows a comparison of the daily load curves of a user before and after the execution of a power package. As can be seen from the attached figure 2, after the electric quantity set is carried out, the total daily load curve of the user tends to be smooth, the peak-valley difference is obviously reduced, and the peak clipping and valley filling effects are obvious.
Table 5 gives the load characteristics of the user before and after the power package is performed. As can be seen from Table 5, after the charge is applied, the peak load value is decreased by 14.12MW, which is 10.6% of the original total peak load value, and the daily peak-to-valley difference rate of the system is 32.02%, which is 22.1% lower than the original data.
TABLE 5 user load characteristics before and after execution of an optimal power package
Figure BDA0002206277820000162
Table 6 shows the change of cost and benefit after the grid company implements the power package. By implementing the electric quantity package, the reduced electric charge income of the power grid company is 614.00 ten thousand yuan, the avoidable capacity income, the avoidable electric quantity income and the system reliability income are 132.40 ten thousand yuan, 323.93 ten thousand yuan and 273.27 ten thousand yuan respectively, and the total profit is 115.61 ten thousand yuan. This indicates that the benefit generated by the electric quantity package is greater than the input cost, and the electric quantity package actively responding to the demand side has a certain practical application value.
TABLE 6 cost-benefit analysis of Power grid company implementing a package of electric quantities
Figure BDA0002206277820000171

Claims (6)

1. A demand side active response oriented electric quantity package optimization design method is characterized by comprising the following steps:
s1: by referring to the mobile tariff package design of the telecommunication industry, an electric quantity package which actively responds towards a demand side is provided;
s2: on the basis of comprehensively considering the influence of the electric charge expenditure and the power utilization mode on the user decision, constructing a comprehensive utility model of the electric quantity package selected by the user;
s3: constructing a selection behavior model of a user for the electric quantity package based on a plurality of Logit models;
s4: constructing a response model of a user for the electric quantity package based on analysis of the electric quantity package on the electric consumption behavior of the user before and after the electric quantity package is implemented;
s5: and establishing an optimization model of the power package design with the minimized system peak-valley difference as an optimization target.
2. The demand side active response oriented electric quantity package optimization design method according to claim 1, characterized in that: in S1, a demand side active response oriented electric quantity package is proposed by referring to the mobile tariff package design in the telecommunication industry, and the specific implementation method is as follows:
the electric quantity package is designed by referring to a mobile tariff package, and certain peak electric quantity and valley electric quantity are packaged and sold to users by utilizing the psychology of saving the electric charge of power users; on the basis of a typical load curve of a user, a reward and punishment mechanism is established: under the conditions of reducing the electricity consumption in the peak period and increasing the electricity consumption in the valley period, the user can purchase more electricity at the same cost, so that the user is encouraged to transfer part of the load in the peak period to the valley period, the peak clipping and valley filling effects are realized, the electricity generation cost is saved, and the resource utilization rate is improved;
the method comprises the steps of dividing an electric quantity package into J grades, and for the package J, the basic charge to be paid by a user is Cj(ii) a Each grade of the electric quantity package comprises two basic attributes: peak time period electric quantity and valley time period electric quantity, each basic attribute has an attribute present quantity, namely the electric quantity which can be used for free under the package, and the peak time period electric quantity is QpjMillet (corn)The time interval electric quantity is Qvj(ii) a Package j contains an amount of electricity Qj＝Qpj+QvjWhen the electricity consumption of the user is in the range, the charging is not carried out on the basis of paying the basic charge, in addition, in order to encourage the user to select the package in the initial stage of the push of the electricity package, the penalty is not carried out on the excess electricity consumption, namely when the electricity consumption of the user exceeds the attribute donation quantity contained in the package, the charging is carried out according to the current peak-valley electricity price mechanism.
3. The demand side active response oriented electric quantity package optimization design method according to claim 1, characterized in that: in S2, on the basis of comprehensively considering the influence of the electricity expense and the electricity utilization mode on the user decision, a comprehensive utility model of the user selection of the electricity package is constructed, and the specific implementation method is as follows:
the utility of the electric charge expenditure is used for measuring the utility brought to the user by the reduction of the electric charge expenditure, and the utility of the power utilization mode is used for measuring the utility of the change of the power utilization mode of the user;
the utility of the user electricity fee expenditure is specifically expressed as:
Figure FDA0002206277810000021
in the formula: thetaijShowing the electricity fee expenditure utility of the ith package selected by the ith user; ci0The electricity fee expenditure of the ith class user before the electricity package is implemented; cijAfter the electric quantity package is implemented, the electricity fee expenditure of the jth package is selected by the ith class user;
the monthly peak time interval electric quantity of the ith class of users before the electric quantity package is implemented is Qpi0The electric quantity in the valley period is Qvi0(ii) a The power quantity in the monthly peak time interval of the ith class of users who select the jth package after the power quantity package is implemented is QpijThe electric quantity in the valley period is QvijThen, there are:
Ci0＝Qpi0×Pp+Qvi0×Pv
Cij＝Cj+|Qpij-Qpj|+×Pp+|Qvij-Qvj|+×Pv
in the formula: | x | non grid+When the value of x is larger than zero, the value of the expression is x; when the value of x is less than or equal to zero, the value of the expression is zero;
after the electric quantity package is carried out, the user responds, the change of the power utilization mode also can influence the utility of the package selected by the user, and the utility of the power utilization mode of the user is expressed as follows:
εij＝-(|Qpij-Qpi0|+|Qvij-Qvi0|)
in the formula: epsilonijShowing the power utilization mode utility of the ith package selected by the ith user;
comprehensively considering two aspects of the utility of the power utilization mode of the user and the utility of the expenditure of the electric charge, the utility of the package grading to the user is as follows:
Vij＝λ1θij+λ2εij
λ1+λ2＝1
in the formula: vijIndicates the utility of the ith class of users to select the jth package, where1Is the weight value of the utility of the user's electric charge expenditure, lambda2The power utilization method is a weight value of the utility of the power utilization mode of the user.
4. The demand side active response oriented electric quantity package optimization design method according to claim 1, characterized in that: in S3, a selection behavior model of the user for the electric quantity package is constructed based on a plurality of Logit models, and the specific implementation method is as follows:
the probability that a user selects a certain package of electric quantity is predicted by an MNL model in a discrete selection model, and the probability that the user selects the certain package can be determined by the utility of the MNL model, so that the probability that a completely rational typical user i selects the package j is as follows:
Figure FDA0002206277810000031
in the formula: pijIs shown asThe probability of the class i user selecting the jth package, μ is a scaling parameter whose value can be reasonably determined by market research and analysis.
5. The demand side active response oriented electric quantity package optimization design method according to claim 1, characterized in that: in S4, based on analysis of power consumption behavior of the user before and after the power package is implemented, a response model of the user for the power package is constructed, and the specific implementation method is as follows:
after a user selects a certain graded electric quantity package, the power utilization behavior of the user is constrained by the grade of the package, and the total power utilization requirement is not changed after the user selects the electric quantity package, namely, the power utilization time is only shifted, namely, the user is constrained by the electric quantity package, and partial peak time period electric quantity is shifted to the valley time period for use, but the total power utilization quantity is not changed; after the user selects the package, the free electric quantity in the package is preferentially used; if the electricity consumption demand of the user is less than the package electricity quantity, the user follows the package setting to use the electricity quantity; when the electricity demand of the user is larger than the package electricity quantity, more parts are used according to the original electricity consumption habit of the user, namely, more parts are used in the peak time period and are still used in the valley time period;
with Qpi0And Qvi0Respectively representing the original peak time interval and valley time interval electricity consumption of the ith class of users, and then the total electricity consumption before the meal is completed is Qi0＝Qpi0+Qvi0(ii) a With QpijAnd QvijRespectively representing the electricity consumption of the peak time interval and the valley time interval after the ith class of users select the electricity package j, and then the total electricity consumption is Qi＝Qpij+Qvij(ii) a Based on the above analysis, the monthly peak time period electric quantity Q of all the power consumers can be obtainedpElectric quantity Q in valley periodvComprises the following steps:
Figure FDA0002206277810000032
Figure FDA0002206277810000033
in the formula: i represents the number of user categories; siIndicating the number of class i users.
6. The demand side active response oriented electric quantity package optimization design method according to claim 1, characterized in that: in S5, an optimization model of the power package design with the minimized system peak-to-valley difference as an optimization target is established, and the optimization model is solved, specifically, the implementation method is as follows:
step 1: target function for constructing electric quantity package optimization design
The optimization model of the electric quantity package design facing the active response of the demand side is shown as the following formula, and the decision variable of the optimization model is the basic charge C graded by each packagejIncluding high peak time period electric quantity QpjAnd off-peak time electric quantity Qvj，
Figure FDA0002206277810000041
In the formula: p (t) is the load at the t hour on the daily load curve,
Figure FDA0002206277810000042
represents the highest load on the daily load curve,
Figure FDA0002206277810000043
represents the minimum load on the daily load curve,
Figure FDA0002206277810000044
peak-to-valley difference representing daily load curve;
step 2: constraint condition for constructing electric quantity package optimization design
(1) Revenue constraint for power grid company to implement power package
Considering from the perspective of a power grid company, the power grid company must gain and cannot lose at least after the power grid company carries out the power package, otherwise, the power grid company does not have the power for carrying out the power package; on one hand, after the electric quantity package is carried out, the electric charge income of a power grid company is reduced to:
on the other hand, after the power grid company realizes peak clipping and valley filling, the power supply cost can be reduced, and the reduced cost is the benefit, including the avoidable capacity benefit, the avoidable electric quantity benefit and the system reliability benefit;
the avoidable capacity gain refers to the average investment cost of the power supply equipment that the grid company can reduce due to the reduction of the highest peak load, and can be expressed as:
B1＝GΔV
in the formula: b is1For avoidable capacity benefits, Δ V is avoidable capacity, G is average unit cost of power supply equipment of the grid company, Δ PiThe peak load value of the ith user reduced in the peak period is shown, I is the total number of users participating in peak clipping and valley filling, sigma is a user simultaneous coefficient, lambda is a spare capacity coefficient of the system, and α is a power distribution loss coefficient of a power grid;
the avoidable electricity yield is the cost saved by the power grid company for reducing the peak electricity quantity purchased, and can be expressed as:
B2＝ρΔQ
ρ＝fpg-fvg
in the formula: rho is the difference between the average on-line electricity price at the peak time and the average on-line electricity price at the valley time, delta Q is the amount of electricity transferred from the peak time to the valley time, fpgAverage on-line electricity price at peak time fvgThe average on-line electricity price in the valley period,
the system reliability gains are that after the power grid company implements the electric quantity package project, the power failure probability is reduced, the system power supply reliability is improved, and the cost for purchasing the rotary standby is reduced, which can be expressed as:
B3＝ΔQ×LOLP×(VOLL-SMP)
in the formula: delta Q is the electric quantity transferred from peak time to valley time, LOLP is the probability of load loss of the power system before the electric quantity package is carried out, the unit is 'day/year', VOLL represents the value of load loss, and SMP is the average price of on-line electricity;
the avoidable cost of the grid company after the electric quantity package is implemented is as follows:
B＝B1+B2+B3
based on the cost benefit analysis of the above electric quantity package, to ensure that the power grid company is not damaged, the constraint condition can be expressed as:
B≥ΔC
(2) peak and valley time peak load constraints
When the optimization solution is performed according to the objective function, the situation that the user excessively responds to the package electricity price is avoided: the load transferred from the peak period to the valley period is excessive, so that a new valley load appears in the original peak period and a new peak load appears in the original valley period; to prevent the user from over-reacting, the constraint can be expressed as:
max Pp(t)＞max Pv(t)
in the formula: max Pp(t) represents the maximum load during peak hours, max Pv(t) represents the highest load of the valley period;
(3) user utility value constraints
When designing targeted package grading for users in various markets, and preventing a high-grade user from selecting a low-grade electric quantity package, setting constraint conditions as follows:
Vii＞Vij(j≠i)(i＝1,2,...,I；j＝1,2,...,J)
in the formula: viiSelecting a utility value, V, for an ith package profile designed for a user of an ith marketijSelecting the utility of the jth package ranking for users in the ith market, the constraint ensures that the utility value is the maximum when the user selects its corresponding package ranking.
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CN111967695B (en) * 2020-09-12 2023-12-12 浙江大学 Peak-valley combined electric power package optimization method for electricity selling company
CN113033953A (en) * 2021-02-07 2021-06-25 国网浙江省电力有限公司金华供电公司 Big data-based user side demand response decision suggestion method
CN113033953B (en) * 2021-02-07 2023-08-25 国网浙江省电力有限公司金华供电公司 User side demand response decision suggestion method based on big data
CN113554268B (en) * 2021-06-10 2024-03-15 合肥工业大学 Method and system for selecting electricity utilization strategies in balanced peak-valley and light-heavy seasons
CN113554268A (en) * 2021-06-10 2021-10-26 合肥工业大学 Method and system for selecting power utilization strategy for balancing peak valley and light and vigorous seasons
CN113538041B (en) * 2021-06-29 2022-10-21 广东电力交易中心有限责任公司 Power package recommendation method and device based on load curve clustering analysis
CN113538041A (en) * 2021-06-29 2021-10-22 广东电力交易中心有限责任公司 Power package recommendation method and device based on load curve clustering analysis
CN114582070A (en) * 2022-02-28 2022-06-03 无锡市恒通电器有限公司 Single-phase guide rail type intelligent electric energy meter supporting quota management
CN114462725B (en) * 2022-04-13 2022-09-02 国网浙江省电力有限公司营销服务中心 Non-direct control type demand side response optimization scheduling method based on dynamic resource pool
CN114462725A (en) * 2022-04-13 2022-05-10 国网浙江省电力有限公司营销服务中心 Non-direct control type demand side response optimization scheduling method based on dynamic resource pool

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