CN106327265A - Industrial user dynamic differential electricity price mechanism for energy saving and emission reduction - Google Patents

Abstract

The invention discloses an industrial user dynamic differential electricity price mechanism for energy saving and emission reduction, and the method introduces the concepts of user demand response and users' satisfaction on the basis of a conventional electricity price mechanism and an electricity pricing principle. The method comprises the steps: building a dynamic differential electricity price model, and carrying out the regulation of the electricity price according to the change of an air quality index; adjusting the electricity price as k times of the original price. From the aspect of demand side response, the invention provides the dynamic differential electricity price regulation and control mechanism for an industrial user, and aims at promoting the user to adjust the behavior of power utilization through the change of electricity price, so as to achieve the purpose of energy saving and emission reduction. In the method, the built electricity regulation and control mechanism adjusts the electricity price according to the degree of air pollution. The severer the air pollution is, the bigger an adjustment coefficient is, and the higher the adjusted electricity price is. During the determining of the adjustment coefficient of the electricity price, a multi-target optimization model which gives comprehensive consideration to the users' satisfaction and the energy saving and emission reduction is built. Moreover, an analytic hierarchy process is used for adjusting a target function according to the characteristics of different users.

Description

A kind of dynamic different pricing of electric power of the industrial user towards energy-saving and emission-reduction mechanism

Technical field

The present invention relates to energy-conserving and emission-cutting technology field, a kind of dynamic difference of the industrial user towards energy-saving and emission-reduction electricity Valency mechanism.

Background technology

Along with the deterioration of air quality, haze weather persistently increases, and in some areas of China, annual haze weather is many Reach tens of even sky.The most serious air pollution problems inherent, makes to safety such as China's transportation, agricultural production, supplies of electric power Become harm in various degree, the most also the healthy of the people is constituted great threat.Investigation shows, industrial discharge and raw coal combustion It is the topmost source of air heavy air pollution process, and thermoelectricity coal consumption accounts for the nearly half of the total coal consumption in the whole nation；Additionally, commercial power Amount accounts for the 70% of China's Analyzing Total Electricity Consumption.Therefore, industrial user be cause air pollution and electric power energy waste main Colony.

China tackles the major measure of air pollution problems inherent at present to be had: reduce Air Pollutant Emission total amount, such as industry Pollution control device is installed by enterprise, and polluted enterprises is limited output；Strict limiting vehicle exhaust emissions, improves traffic administration； Optimizing City is planned, reduces dust from construction sites；Improve Air Quality Evaluation standard and early warning mechanism etc..China is administering air pollution During the compulsive measures taked, often do not reach preferable effect, many user's not active response national policy measures, Producing the most in violation of rules and regulations, cause the wasting of resources, the most a large amount of excessive discharges of pollutants, have increased the weight of environmental pollution.

Power industry is as the basic industry of national economy, and its integral status is related to energy security and the economy of country Social Development State.Main pricing method has: maximum profit method of fixing price, marginal cost price, accounting cost method of fixing price, two Portion's method of fixing price etc..Main tariff structure has: unique power price, two-part rate system price, Peak-valley TOU power price, step price, difference Other electricity price, power factor adjust electricity price etc..

Different pricing of electric power system is power supply enterprise according to the user's different demands to power product, and the different time, Point, the concrete condition of user power utilization, suitable modified basis price sells the strategy of electric energy product.The difference electricity that China carries out at present Valency is the electrovalence policy formulated for high energy-consuming industry, and its target is to limit the pell-mell development of high energy consumption enterprise, reduces energy consumption, Improve the output value of specific energy consumption.Traditional different pricing of electric power system be highly energy-consuming class enterprise is divided into permission and encourage class, restricted, Three classes out of category, for electricity consumption restricted and out of category is performed of a relatively high sales rate of electricity, its executive mode is the most solid Fixed.

This patent, from the angle of Demand Side Response, considers electricity consumption satisfaction and the need of energy-saving and emission-reduction of user Ask, for the most serious air pollution problems inherent, propose a kind of dynamic different pricing of electric power of the industrial user towards energy-saving and emission-reduction.This electricity Depending on whether valency performs according to air quality situation, within the regulation and control cycle, if air quality persistently preferably time do not start this difference Electricity price, and when air quality is poor, start this different pricing of electric power, and the adjustment dynamics of different pricing of electric power is relative with the pollution level of air Should；The specific object according to user is also needed to, as energy consumption, blowdown flow rate, social status etc. come during this external startup different pricing of electric power Dynamically to the adjustment dynamics revising different pricing of electric power.Thus reaching energy-saving and emission-reduction, alleviate the same of the most serious air pollution Time, take into account production and the income of industrial user, promote user actively to carry out energy-saving and emission-reduction transformation, accelerate industry restructuring and protect Barrier social stability.

Summary of the invention

For problem above, the invention provides a kind of dynamic different pricing of electric power of the industrial user towards energy-saving and emission-reduction mechanism, The present invention, from the angle of Demand Side Response, proposes a kind of dynamic different pricing of electric power regulatory mechanism towards industrial user, it is intended to Promoting user to adjust electricity consumption behavior by the change of electricity price, to reach the purpose of energy-saving and emission-reduction, the electricity price set up in the present invention is adjusted Control mechanism adjusts electricity price according to air pollution degree, and the most serious regulation coefficient of air pollution is the biggest, and the electricity price after adjustment is the highest. When determining bidding price adjustment coefficient, set up the Model for Multi-Objective Optimization considering user satisfaction and energy-saving and emission-reduction, and utilize Object function is adjusted by analytic hierarchy process (AHP) for the own characteristic of different user, for above-mentioned Optimized model, use based on Multi-objective genetic algorithm solves, and is put forward machine-processed reasonability and effectiveness by Example Verification, can effectively solve Problem in background technology.

For achieving the above object, the present invention provides following technical scheme: a kind of industrial user towards energy-saving and emission-reduction is dynamic Different pricing of electric power mechanism, on the basis of existing Price Mechanisms and electricity price pricing principle, introduces user's request response and user is satisfied The concept of degree, sets up dynamic different pricing of electric power model and regulates and controls electricity price according to the change of air quality index, electricity price is adjusted Whole for original electricity price k times；Its establishment step is as follows:

First object function is established: the comprehensive satisfaction maximum expression formula of user is

F₁=max S₀=max [γ₁S_m+γ₂S_c] (1)

For the ease of analyzing, by object function F₁Become the form of minimizing, it may be assumed that

F₁=min [-S₀]=min [-γ₁S_m-γ₂S_c] (2)

In formula: S₀Comprehensive satisfaction for user；S_mFor user power utilization mode satisfaction；S_cPay for user power utilization expense Satisfaction；γ₁Weights for user power utilization mode satisfaction；γ₂For the weights of demand charge expenditure satisfaction, γ₁+γ₂=1；

The expression formula that total need for electricity amount of user is minimum is:

$F_2=\min \underset{i=1}{\overset{n}{\Σ}}{Q_i}^{\′}---\left(3\right)$

In formula: Q is user's total need for electricity amount；Q_iUser power utilization demand for the i period；

Making when air quality index is higher, the need for electricity amount amount lowest expression of user is:

$F_3=\min \underset{j=1}{\overset{N}{\Σ}}{Q_j}^{\′}---\left(4\right)$

In formula: Q_j' for air quality index need for electricity amount of user time higher, N is that air quality refers in the regulation and control cycle Natural law when number is higher；

Then in view of cost of electricity-generating and the ability to bear of user, when formulating bidding price adjustment scheme, to bidding price adjustment model Retrain as follows:

p_min<p_i'<p_max (5)

In formula: p_min, p_maxThe minima of the t period electricity price specified for supervision department and maximum；

Finally set up different pricing of electric power regulation-control model:

$\left\{\begin{array}{c}{F}_1=min\&lsqb;-{\mathrm{\&gamma;}}_1{S}_m-{\mathrm{\&gamma;}}_2{S}_c\&rsqb;\\ F_2=\min \underset{i=1}{\overset{n}{\&Sigma;}}{Q_i}^{\&prime;}\\ F_3=\min \underset{j=1}{\overset{N}{\&Sigma;}}{Q_j}^{\&prime;}\end{array}\right.---\left(6\right)$

$s.t.\left\{\begin{array}{c}{p}_{\min }<{p_i}^{\&prime;}<p_{\max }\\ {\mathrm{\&gamma;}}_1+{\mathrm{\&gamma;}}_2=1\end{array}\right.---\left(7\right).$

Preferably, described object function is divided into and includes user power utilization mode satisfaction and demand charge expenditure satisfaction:

(a) user power utilization mode satisfaction: user power utilization mode satisfaction is built upon adjusting electricity and former load curve On difference basis on, be embodied as:

$S_m=1-\frac{\underset{i=1}{\overset{n}{\&Sigma;}}|{Q_i}^{\&prime;}-Q_i|}{\underset{i=1}{\overset{n}{\&Sigma;}}{Q}_i}---\left(8\right)$

(b) demand charge expenditure satisfaction: user power utilization expense expenditure satisfaction is to weigh the change of demand charge expenditure The index of amount, is embodied as:

$S_c=1-\frac{\underset{i=1}{\overset{n}{\&Sigma;}}({Q_i}^{\&prime;}{p_i}^{\&prime;}-Q_i{p}_i)}{\underset{i=1}{\overset{n}{\&Sigma;}}{Q}_i{p}_i}---\left(9\right)$

In formula: C (P₀) be user under original electrovalence policy electricity cost expenditure, it is former electricity price P₀Function；C(P₀') Paying for the electricity cost of user after adjusting electricity price, it is new electricity price P₀' function.

Preferably, use analytic hierarchy process (AHP) and multi-objective genetic algorithm to dynamic different pricing of electric power model solution:

First considering user property, Modifying model based on analytic hierarchy process (AHP) is as follows:

At the object function that formula (6) is set up, have three object functions: improve user power utilization satisfaction, save electric power energy, Alleviate air pollution problems inherent, when concrete formulation bidding price adjustment scheme, need according to different user types and attribute, in conjunction with actual Situation, gives different weights to object function, carries out a certain degree of correction, thus control the dynamics of bidding price adjustment, by mesh Scalar functions is adjusted to:

Wherein:Represent the weights of three object functions respectively.And it is different industrial Family is correspondingWill be different；

When different industrial users participates in different pricing of electric power regulation and control, analytic hierarchy process (AHP) is used to determine the weight of each object functionBasic step is as follows:

(1) hierarchical structure model is set up, including

Destination layer: adjust object function weight；

Decision-making level: unit output value power consumption；Unit output value blowdown flow rate；The purification difficulty of pollutant；Social status；Geographical position Put；

Solution layer: improve satisfaction；Reduce power budget；Reduce disposal of pollutants；

(2) structure pairwise comparison matrix: utilized the pairwise comparison matrix of 1～9 dimensional configurations different layers by expert；

(3) calculate single rank order filtering and do consistency check；

(4) calculate total rank order filtering and do consistency check；

(5) weight vector of numerical procedure layer sequence total to destination layer, the i.e. weight of object function

It is then based on multi-objective genetic algorithm optimum for pareto to solve, uses MATLAB to calculate, have invoked Gamultiobj function in MATLAB, concrete step is as follows:

(1) bidding price adjustment cycle n is set；

(2) if estimate to there is AQI value more than 200 in the bidding price adjustment cycle, then start this different pricing of electric power, do not open Dynamic；Wherein AQI refers to air quality index, according to the height of air quality index, air quality situation is divided into four grades: Well (0-100), light intermediate pollution (101-200), serious pollution (201-300), severe contamination (more than 300), bidding price adjustment Coefficient k is also classified into four grades the most corresponding.

(3) if starting this different pricing of electric power, then bidding price adjustment coefficient k is distributed according to the concrete data of AQI_i, i in the present invention =1,2,3,4；

(4) according to customer response model, user's need for electricity amount after bidding price adjustment is calculated；

(5) setting seeks the object function of the best electric price regulation coefficient, including user satisfaction F₁, total need for electricity amount F₂, empty Need for electricity amount F when makings volume index is more than 200₃, and the correction weight of three object functions

(6) constraints of optimization, the i.e. bound of bidding price adjustment coefficient k are set；

(7) call gamultiobj function to be optimized and solve, obtain a series of pareto optimal solution, i.e. before pareto End.

(8) bidding price adjustment coefficient k is obtained_iOptimal solution.

Compared with prior art, the invention has the beneficial effects as follows: the present invention, from the angle of Demand Side Response, proposes one Plant the dynamic different pricing of electric power regulatory mechanism towards industrial user, it is intended to promote user to adjust electricity consumption behavior by the change of electricity price, To reach the purpose of energy-saving and emission-reduction, the electricity price regulatory mechanism set up in the present invention adjusts electricity price, air according to air pollution degree Polluting the most serious regulation coefficient the biggest, the electricity price after adjustment is the highest, when determining bidding price adjustment coefficient, sets up and considers use Family satisfaction and the Model for Multi-Objective Optimization of energy-saving and emission-reduction, and utilize analytic hierarchy process (AHP) for the own characteristic of different user to mesh Scalar functions is adjusted, and for above-mentioned Optimized model, uses and solves based on multi-objective genetic algorithm, and pass through Example Verification Carried reasonability and the effectiveness of mechanism.

Accompanying drawing explanation

Fig. 1 is the hierarchical structure model figure that the present invention adjusts object function weight；

Fig. 2 is enterprise's A air quality index (AQI) of the present invention and load comparisons's bar diagram；

Fig. 3 is enterprise B air quality index (AQI) of the present invention and load comparisons's bar diagram.

Detailed description of the invention

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Describe, it is clear that described embodiment is only a part of embodiment of the present invention rather than whole embodiments wholely.Based on Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under not making creative work premise Embodiment, broadly falls into the scope of protection of the invention.

Embodiment:

Before setting up dynamic different pricing of electric power model, first set up user's request response model based on discrete captivation.

Reasonably electricity price system is the Important Economic means in dsm, this is because price be connect supply side with The tie of Demand-side, is also the lever of regulatory demand amount and quantity delivered, reasonably utilizes the demand response in electricity market to help In ensureing stablizing of electrical network.

For the quantitative description user response to electricity price, introduce Price elasticity matrix and represent that user needs for the price of electricity price Ask elastic.Period 1～n need for electricity amount can represent with electricity price elastic matrix E with the relation of electricity price:

In formula: hop count when n is；ε_iiAnd ε_ijIt is respectively electricity needs price self-elasticity coefficient and mutual coefficient of elasticity；

After can obtaining adjustment electricity price according to formula (1.1), the demand response model of user is:

$\left[\begin{array}{c}{Q_1}^{\&prime;}\\ {Q_2}^{\&prime;}\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ {Q_n}^{\&prime;}\end{array}\right]=\frac{1}{n}E\left[\begin{array}{c}{\mathrm{\&Delta;p}}_1/p_1\\ {\mathrm{\&Delta;p}}_2/p_2\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ {\mathrm{\&Delta;p}}_n/p_n\end{array}\right]+\left[\begin{array}{c}{Q}_1\\ Q_2\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ Q_n\end{array}\right]---\left(1.2\right)$

In formula: Q_i' and Q_iBefore and after being respectively bidding price adjustment, the user power utilization demand of i period；Δp_i=p'_i-p_i=(k- 1)p_iFor the variable quantity of electricity price, k is the regulation coefficient of electricity price；

In order to calculate electricity needs price self-elasticity coefficient ε_iiWith mutual coefficient of elasticity ε_ij, introduce the concept of discrete captivation. The main meaning of discrete captivation be the determiner of the market share of commodity be its captivation to consumer, captivation The biggest then market share is the highest.According to captivation model market share S_aiIt is represented by:

S_ai=R_ai/R_i (1.3)

In formula: R_aiFor the captivation of commodity a during time i；R_iCommodity a and all similar commodity thereof are comprised during for time i Total captivation；

Self-elasticity coefficient ε in conjunction with the concept electricity needs price of the market share_iiWith mutual coefficient of elasticity ε_ijFormula be:

${\mathrm{\&epsiv;}}_{ii}=\frac{{\mathrm{\&Delta;S}}_{ai}/S_{ai}}{{\mathrm{\&Delta;p}}_i/p_i}---\left(1.4\right)$

${\mathrm{\&epsiv;}}_{ij}=\frac{{\mathrm{\&Delta;S}}_{ai}/S_{ai}}{{\mathrm{\&Delta;p}}_j/p_j}---\left(1.5\right)$

In formula: p_iAnd p_j(i, j=1,2 ..., n) represent i period and the reference value of j period price respectively；

Based on discrete captivation model, the problem that affects of need for electricity amount can be converted into electricity price by electricity price to be needed electricity consumption The captivation of the amount of asking affects problem.Foundation multiplication competitive interactions (multiplicative competitive interaction, MCI) model, demand price discrete captivation model is represented by:

$R_{ai}=\exp \left({\mathrm{\&alpha;}}_i\right)\&CenterDot;\underset{n=1}{\overset{m}{\&Pi;}}{p}_n^{{\mathrm{\&beta;}}_n}\&CenterDot;{\mathrm{\&mu;}}_i---\left(1.6\right)$

In formula: R_aiFor the electricity price of the period i captivation to need for electricity amount；Exp () is power function；α_iElectricity for period i The valency fixed effect coefficient to demand captivation；β_nFor the electricity price of period n demand captivation affected coefficient；p_nFor time The electricity price of section n, n=1 ..., m；μ_iError term for period i electricity price；

Formula (1.5) is substituted into formula (1) can obtain:

$S_{ai}=\&lsqb;\exp \left({\mathrm{\&alpha;}}_i\right)\&CenterDot;\underset{n=1}{\overset{m}{\&Pi;}}{p}_n^{{\mathrm{\&beta;}}_n}\&CenterDot;{\mathrm{\&mu;}}_i\&rsqb;/\&lsqb;\underset{j=1}{\overset{m}{\&Sigma;}}\exp \left({\mathrm{\&alpha;}}_j\right)\&CenterDot;\underset{n=1}{\overset{m}{\&Pi;}}{p}_n^{{\mathrm{\&beta;}}_n}\&CenterDot;{\mathrm{\&mu;}}_j\&rsqb;---\left(1.7\right)$

Formula (1.7) is substituted into formula (1.4), (1.5) available electricity needs price from bullet system property number ε_iiWith the most elastic system Number ε_ijSpecific formula for calculation:

${\mathrm{\&epsiv;}}_{ii}={\mathrm{\&beta;}}_i-\frac{{\mathrm{\&beta;}}_i{e}^{{\mathrm{\&alpha;}}_i}\underset{n=1}{\overset{m}{\&Pi;}}{p}_n^{{\mathrm{\&beta;}}_n}\&CenterDot;{\mathrm{\&mu;}}_i}{\underset{j=1}{\overset{m}{\&Sigma;}}{e}^{{\mathrm{\&alpha;}}_i}{p}_n^{{\mathrm{\&beta;}}_n}\&CenterDot;{\mathrm{\&mu;}}_j}={\mathrm{\&beta;}}_i(1-S_{ai})---\left(1.8\right)$

${\mathrm{\&epsiv;}}_{ij}={\mathrm{\&beta;}}_i-\frac{{\mathrm{\&beta;}}_i{e}^{{\mathrm{\&alpha;}}_j}\underset{n=1}{\overset{m}{\&Pi;}}{p}_n^{{\mathrm{\&beta;}}_n}\&CenterDot;{\mathrm{\&mu;}}_j}{\underset{j=1}{\overset{m}{\&Sigma;}}{e}^{{\mathrm{\&alpha;}}_j}{p}_n^{{\mathrm{\&beta;}}_n}\&CenterDot;{\mathrm{\&mu;}}_j}=-{\mathrm{\&beta;}}_i{S}_{aj}---\left(1.9\right)$

The invention provides a kind of dynamic different pricing of electric power of the industrial user towards energy-saving and emission-reduction mechanism, at existing Price Mechanisms And on the basis of electricity price pricing principle, introduce user's request response and the concept of user satisfaction, set up dynamic different pricing of electric power mould Electricity price is regulated and controled by type according to the change of air quality index, by k times that bidding price adjustment is original electricity price；Its establishment step As follows:

First object function is established: the comprehensive satisfaction maximum expression formula of user is

F₁=max S₀=max [γ₁S_m+γ₂S_c] (1)

For the ease of analyzing, by object function F₁Become the form of minimizing, it may be assumed that

F₁=min [-S₀]=min [-γ₁S_m-γ₂S_c] (2)

In formula: S₀Comprehensive satisfaction for user；S_mFor user power utilization mode satisfaction；S_cPay for user power utilization expense Satisfaction；γ₁Weights for user power utilization mode satisfaction；γ₂For the weights of demand charge expenditure satisfaction, γ₁+γ₂=1；

The expression formula that total need for electricity amount of user is minimum is:

$F_2=\min \underset{i=1}{\overset{n}{\&Sigma;}}{Q_i}^{\&prime;}---\left(3\right)$

In formula: Q is user's total need for electricity amount；Q_iUser power utilization demand for the i period；

Making when air quality index is higher, the need for electricity amount amount lowest expression of user is:

$F_3=\min \underset{j=1}{\overset{N}{\&Sigma;}}{Q_j}^{\&prime;}---\left(4\right)$

In formula: Q_j' for air quality index need for electricity amount of user time higher, N is that air quality refers in the regulation and control cycle Natural law when number is higher；

Then in view of cost of electricity-generating and the ability to bear of user, when formulating bidding price adjustment scheme, to bidding price adjustment model Retrain as follows:

p_min<p_i'<p_max (5)

In formula: p_min, p_maxThe minima of the t period electricity price specified for supervision department and maximum；

Finally set up different pricing of electric power regulation-control model:

$\left\{\begin{array}{c}{F}_1=min\&lsqb;-{\mathrm{\&gamma;}}_1{S}_m-{\mathrm{\&gamma;}}_2{S}_c\&rsqb;\\ F_2=\min \underset{i=1}{\overset{n}{\&Sigma;}}{Q_i}^{\&prime;}\\ F_3=\min \underset{j=1}{\overset{N}{\&Sigma;}}{Q_j}^{\&prime;}\end{array}\right.---\left(6\right)$

$s.t.\left\{\begin{array}{c}{p}_{\min }<{p_i}^{\&prime;}<p_{\max }\\ {\mathrm{\&gamma;}}_1+{\mathrm{\&gamma;}}_2=1\end{array}\right.---\left(7\right).$

Preferably, described object function is divided into and includes user power utilization mode satisfaction and demand charge expenditure satisfaction:

(a) user power utilization mode satisfaction: user power utilization mode satisfaction is built upon adjusting electricity and former load curve On difference basis on, be embodied as:

$S_m=1-\frac{\underset{i=1}{\overset{n}{\&Sigma;}}|{Q_i}^{\&prime;}-Q_i|}{\underset{i=1}{\overset{n}{\&Sigma;}}{Q}_i}---\left(8\right)$

(b) demand charge expenditure satisfaction: user power utilization expense expenditure satisfaction is to weigh the change of demand charge expenditure The index of amount, is embodied as:

$S_c=1-\frac{\underset{i=1}{\overset{n}{\&Sigma;}}({Q_i}^{\&prime;}{p_i}^{\&prime;}-Q_i{p}_i)}{\underset{i=1}{\overset{n}{\&Sigma;}}{Q}_i{p}_i}---\left(9\right)$

In formula: C (P₀) be user under original electrovalence policy electricity cost expenditure, it is former electricity price P₀Function；C(P₀') Paying for the electricity cost of user after adjusting electricity price, it is new electricity price P₀' function.

Preferably, use analytic hierarchy process (AHP) and multi-objective genetic algorithm to dynamic different pricing of electric power model solution:

First considering user property, Modifying model based on analytic hierarchy process (AHP) is as follows:

At the object function that formula (6) is set up, have three object functions: improve user power utilization satisfaction, save electric power energy, Alleviate air pollution problems inherent, when concrete formulation bidding price adjustment scheme, need according to different user types and attribute, in conjunction with actual Situation, gives different weights to object function, carries out a certain degree of correction, thus control the dynamics of bidding price adjustment, by mesh Scalar functions is adjusted to:

Wherein:Represent the weights of three object functions respectively.And it is different industrial Family is correspondingWill be different；

When different industrial users participates in different pricing of electric power regulation and control, analytic hierarchy process (AHP) is used to determine the weight of each object functionBasic step is as follows:

(1) hierarchical structure model is set up, including

Destination layer: adjust object function weight；

Decision-making level: unit output value power consumption；Unit output value blowdown flow rate；The purification difficulty of pollutant；Social status；Geographical position Put；

Solution layer: improve satisfaction；Reduce power budget；Reduce disposal of pollutants；

(2) structure pairwise comparison matrix: utilized the pairwise comparison matrix of 1～9 dimensional configurations different layers by expert；

(3) calculate single rank order filtering and do consistency check；

(4) calculate total rank order filtering and do consistency check；

(5) weight vector of numerical procedure layer sequence total to destination layer, the i.e. weight of object function

It is then based on multi-objective genetic algorithm optimum for pareto to solve, uses MATLAB to calculate, have invoked Gamultiobj function in MATLAB, concrete step is as follows:

(1) bidding price adjustment cycle n is set；

(2) if estimate to there is AQI value more than 200 in the bidding price adjustment cycle, then start this different pricing of electric power, do not open Dynamic；Wherein AQI refers to air quality index, according to the height of air quality index, air quality situation is divided into four grades: Well (0-100), light intermediate pollution (101-200), serious pollution (201-300), severe contamination (more than 300), bidding price adjustment Coefficient k is also classified into four grades the most corresponding.

(3) if starting this different pricing of electric power, then bidding price adjustment coefficient k is distributed according to the concrete data of AQI_i, i in the present invention =1,2,3,4；

(4) according to customer response model, user's need for electricity amount after bidding price adjustment is calculated；

(5) setting seeks the object function of the best electric price regulation coefficient, including user satisfaction F₁, total need for electricity amount F₂, empty Need for electricity amount F when makings volume index is more than 200₃, and the correction weight of three object functions

(6) constraints of optimization, the i.e. bound of bidding price adjustment coefficient k are set；

(7) call gamultiobj function to be optimized and solve, obtain a series of pareto optimal solution, i.e. before pareto End.

(8) bidding price adjustment coefficient k is obtained_iOptimal solution.

Present invention principle based on multi-objective genetic algorithm optimum for pareto is as follows:

For the concept of accurate description multiple-objection optimization, the present invention carries out following several definition:

Definition 1:Pareto domination: and if only if forThere is u_i≤v_i, and at least one definitely Inequality is set up, then claim vector u=(u₁,u₂,…,u_m) domination (or non-be inferior to) vector v=(v₁,v₂,…,v_m)。

Definition 2:Pareto is optimum: if x is ∈ S, and there is not the solution more superior than x in S, then claiming x is that Pareto is optimum Solve.

Definition 3:Pareto optimum collection: the collection of the composition of all Pareto optimal solutions is collectively referred to as Pareto optimum collection.

Definition 4:Pareto front end: the region that target function value corresponding to all Pareto optimal solutions is formed is referred to as Pareto front end.

The solution of multi-objective optimization question is frequently not unique, but there is an optimal solution set, and in set, unit is called usually For Pareto optimal solution.The corresponding object vector of each solution in multi-objective optimization question, so-called Pareto optimal solution is just It it is to there is not such solution so that the object vector that the object vector of its correspondence is corresponding less than Pareto optimal solution.

Genetic algorithm (Genetic Algorithm) originates from the study of computer simulation being carried out biosystem, it It is the random global search that grows up of natural imitation circle biological evolution mechanism and optimization method, has used for reference Darwinian theory of evolution With Mendelian theory of heredity.The genetic algorithm of multiple-objection optimization, is to owning in population based on concept optimum for pareto Individuality is ranked up, and uses optimal solution conversation strategy to solve, and generation is positioned at the non-bad of pareto front end at the end of calculating Disaggregation, for all solutions in Noninferior Solution Set, has identical superiority-inferiority, policymaker select according to reality application needs Select.

Sample calculation analysis:

According to " national economy and social development statistical report in 2010 ", highly energy-consuming trade is mainly: chemical raw material and change Learn goods manufacturing industry, nonmetallic grounded module, ferrous metal smelting and rolling processing industry, non-ferrous metal metallurgy and calendering processing Industry, PETROLEUM PROCESSING coking and nuclear fuel processing industry, the production and supply industry of electric power heating power.And high pollution industry be mainly thermoelectricity, Iron and steel, cement, petrochemical industry, chemical industry, non-ferrous metal metallurgy etc..Wherein, part industry had both belonged to highly energy-consuming trade, belonged to again high pollution Industry.

The present invention uses the air quality index data in somewhere and the power load data of enterprise A and enterprise B and blowdown flow rate For example data, the effectiveness of the checking present invention carried state different pricing of electric power mechanism.Wherein enterprise A is high pollution highly energy-consuming enterprise Industry, and distance city is relatively near, less for local Economic Contribution, social status is the highest；Enterprise B is high energy-consuming enterprises, distance Farther out and relatively big to local Economic Contribution, social status is higher in city.When calculating bidding price adjustment coefficient k, it is respectively adopted tune Whole object function weight with do not adjust object function weight two schemes, and contrast.In view of current AQI predict technical Problem, the predetermined period used is 7 days.Initial data is as shown in the table: (electricity price is 1 yuan/KW h).

Table 1 initial data

Date/sky	1	2	3	4	5	6	7
								AQI	167	273	324	256	89	86	189
Enterprise A load/MW	30.7	32.4	33.6	30.1	29.6	29.1	32.3
								Enterprise B load/MW	52.1	34.2	53.1	49.8	50.5	51.8	49.3

(1) enterprise A

Scheme one: when using analytic hierarchy process (AHP) to adjust object function weight

According to the practical situation of enterprise, structure pairwise comparison matrix is as follows:

$\begin{array}{cc}B=\left[\begin{array}{ccccc}1& 1/7& 1/5& 1/5& 1\\ 7& 1& 1& 1& 7\\ 5& 1& 1& 1& 5\\ 5& 1& 1& 1& 5\\ 1& 1/7& 1/5& 1/5& 1\end{array}\right]& C_1=\left[\begin{array}{ccc}1& 5& 3\\ 1/5& 1& 1/2\\ 1/3& 2& 1\end{array}\right]\end{array}$

$\begin{array}{cc}{C}_2=\left[\begin{array}{ccc}1& 1/5& 2\\ 5& 1& 5\\ 1/2& 1/5& 1\end{array}\right]& C_3=\left[\begin{array}{ccc}1& 1/2& 1/5\\ 2& 1& 1/3\\ 5& 3& 1\end{array}\right]\end{array}$

$\begin{array}{cc}{C}_4=\left[\begin{array}{ccc}1& 1/2& 1/5\\ 2& 1& 1/3\\ 5& 3& 1\end{array}\right]& C_5=\left[\begin{array}{ccc}1& 1/3& 1/5\\ 3& 1& 1/2\\ 5& 2& 1\end{array}\right]\end{array}$

Above-mentioned matrix is all by consistency check, and each target weight finally determined by analytic hierarchy process (AHP) is:Be computed gained electricity price regulation and control coefficient be k=(0.96,1.50,1.98,1.50, 0.50,0.50,0.96).

Scheme two: when not using analytic hierarchy process (AHP) to adjust object function weight, being computed gained electricity price regulation and control coefficient is k= (0.98,1.50,1.99,1.50,0.26,0.26,0.98).

Two schemes Comparative result is as follows:

Table 2 two schemes Comparative result

Table 3 two schemes target function value

	F1	F2/MW	F3/MW
				Initially	1	217.8	76.1
Scheme one	1.05	200.9	65.9
				Scheme two	0.98	207.3	66.1

Be can be seen that by result, owing to this enterprise is high pollution high energy-consuming enterprises, the Price Mechanisms that the application present invention is carried enters Row electricity price regulation and control after, when air quality index is relatively low, load has risen, when air quality index is higher, load under Fall, and the effect that scheme one is in terms of energy-saving and emission-reduction is more preferable.

(2) enterprise B

Scheme one: when using analytic hierarchy process (AHP) to adjust object function weight

According to the practical situation of enterprise, structure pairwise comparison matrix is as follows:

$\begin{array}{cc}B=\left[\begin{array}{ccccc}1& 1/3& 2& 2& 1\\ 3& 1& 4& 4& 3\\ 1/2& 1/4& 1& 1& 1/2\\ 1/2& 1/4& 1& 1& 1/2\\ 1& 1/3& 2& 2& 1\end{array}\right]& C_1=\left[\begin{array}{ccc}1& 2& 5\\ 1/2& 1& 3\\ 1/5& 1/3& 1\end{array}\right]\end{array}$

$\begin{array}{cc}{C}_2=\left[\begin{array}{ccc}1& 1/3& 2\\ 3& 1& 5\\ 1/2& 1/5& 1\end{array}\right]& C_3=\left[\begin{array}{ccc}1& 2& 1/3\\ 1/2& 1& 1/3\\ 3& 3& 1\end{array}\right]\end{array}$

$\begin{array}{cc}{C}_4=\left[\begin{array}{ccc}1& 2& 1/3\\ 1/2& 1& 1/3\\ 3& 3& 1\end{array}\right]& C_5=\left[\begin{array}{ccc}1& 1/3& 1\\ 3& 1& 3\\ 1& 1/3& 1\end{array}\right]\end{array}$

Above-mentioned matrix is all by consistency check, and each target weight finally determined by analytic hierarchy process (AHP) is:Be computed gained electricity price regulation and control coefficient be k=(0.99,1.48,1.98,1.48, 0.38,0.38,0.99).

Scheme two: when not using analytic hierarchy process (AHP) to adjust object function weight, being computed gained electricity price regulation and control coefficient is k= (0.97,1.46,1.99,1.46,0.28,0.28,0.97).

Two schemes Comparative result is as follows:

Table 4 two schemes Comparative result

Table 5 two schemes target function value

	F1	F2/MW	F3/MW
				Initially	1	340.8	137.1
Scheme one	1.13	323.8	92.8
				Scheme two	1.03	330.5	93.9

Be can be seen that by result, owing to this enterprise is high energy-consuming enterprises, the Price Mechanisms that the application present invention is carried carries out electricity price After regulation and control, when air quality index is relatively low, load has risen, when air quality index is higher, under load has by a relatively large margin Fall, and the effect that scheme one is in terms of energy-saving and emission-reduction is more preferable.

Conclusion

The air pollution problems inherent the most serious for China and energy shortage problem, the present invention is at existing Price Mechanisms And on the basis of electricity price pricing principle, introduce user's request response and the concept of user satisfaction, it is proposed that a kind of towards industry The dynamic different pricing of electric power model of user.This model with energy-saving and emission-reduction as target, consider user electricity consumption satisfaction and from Body feature, has dynamic characteristic.The feasibility of the present invention dynamic different pricing of electric power of carried electricity price and having finally by Example Verification Effect property.

The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention Any amendment, equivalent and the improvement etc. made within god and principle, should be included within the scope of the present invention.

Claims (3)

1. the dynamic different pricing of electric power of the industrial user towards energy-saving and emission-reduction mechanism, it is characterised in that at existing Price Mechanisms and On the basis of electricity price pricing principle, introduce user's request response and the concept of user satisfaction, set up dynamic different pricing of electric power model Electricity price is regulated and controled by the change according to air quality index, by k times that bidding price adjustment is original electricity price；Its establishment step is such as Under:

First object function is established: the comprehensive satisfaction maximum expression formula of user is

F₁=max S₀=max [γ₁S_m+γ₂S_c] (1)

For the ease of analyzing, by object function F₁Become the form of minimizing, it may be assumed that

F₁=min [-S₀]=min [-γ₁S_m-γ₂S_c] (2)

In formula: S₀Comprehensive satisfaction for user；S_mFor user power utilization mode satisfaction；S_cSatisfied for user power utilization expense expenditure Degree；γ₁Weights for user power utilization mode satisfaction；γ₂For the weights of demand charge expenditure satisfaction, γ₁+γ₂=1；

The expression formula that total need for electricity amount of user is minimum is:

$F_2=\min \underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{Q_i}^{\&prime;}---\left(3\right)$

In formula: Q is user's total need for electricity amount；Q_iUser power utilization demand for the i period；

Making when air quality index is higher, the need for electricity amount amount lowest expression of user is:

$F_3=\min \underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{Q_j}^{\&prime;}---\left(4\right)$

In formula: Q_j' for air quality index need for electricity amount of user time higher, N is that air quality index is higher in the regulation and control cycle Time natural law；

Then in view of cost of electricity-generating and the ability to bear of user, when formulating bidding price adjustment scheme, bidding price adjustment model is carried out Following constraint:

p_min<p_i'<p_max (5)

In formula: p_min, p_maxThe minima of the t period electricity price specified for supervision department and maximum；

Finally set up different pricing of electric power regulation-control model:

$\left\{\begin{array}{c}{F}_1=\min \&lsqb;-{\mathrm{\&gamma;}}_1{S}_m-{\mathrm{\&gamma;}}_2{S}_c\&rsqb;\\ F_2=\min \underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{Q_i}^{\&prime;}\\ F_3=\min \underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{Q_j}^{\&prime;}\end{array}\right.---\left(6\right)$

$s.t.\left\{\begin{array}{c}{p}_{\min }<{p_i}^{\&prime;}<p_{\max }\\ {\mathrm{\&gamma;}}_1+{\mathrm{\&gamma;}}_2=1\end{array}\right.---\left(7\right).$

A kind of dynamic different pricing of electric power of the industrial user towards energy-saving and emission-reduction the most according to claim 1 mechanism, its feature exists In: described object function is divided into and includes user power utilization mode satisfaction and demand charge expenditure satisfaction:

(a) user power utilization mode satisfaction: user power utilization mode satisfaction is built upon adjusting on electricity and former load curve On difference basis, it is embodied as:

$S_m=1-\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\left|{Q_i}^{\&prime;}-Q_i\right|}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{Q}_i}---\left(8\right)$

(b) demand charge expenditure satisfaction: user power utilization expense expenditure satisfaction is to weigh the variable quantity of demand charge expenditure Index, is embodied as:

$S_c=1-\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({Q_i}^{\&prime;}{p_i}^{\&prime;}-Q_i{p}_i)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{Q}_i{p}_i}---\left(9\right)$

In formula: C (P₀) be user under original electrovalence policy electricity cost expenditure, it is former electricity price P₀Function；C(P₀') for adjusting The electricity cost expenditure of user after whole electricity price, it is new electricity price P₀' function.

A kind of dynamic different pricing of electric power of the industrial user towards energy-saving and emission-reduction the most according to claim 1 mechanism, its feature exists In: use analytic hierarchy process (AHP) and multi-objective genetic algorithm to dynamic different pricing of electric power model solution:

First considering user property, Modifying model based on analytic hierarchy process (AHP) is as follows:

At the object function that formula (6) is set up, there are three object functions: improve user power utilization satisfaction, save electric power energy, alleviation Air pollution problems inherent, when concrete formulation bidding price adjustment scheme, needs according to different user types and attribute, in conjunction with actual feelings Condition, gives different weights to object function, carries out a certain degree of correction, thus control the dynamics of bidding price adjustment, by target Function is adjusted to:

Wherein:Represent the weights of three object functions respectively.And different industrial users couple AnswerWill be different；

When different industrial users participates in different pricing of electric power regulation and control, analytic hierarchy process (AHP) is used to determine the weight of each object functionBasic step is as follows:

(1) hierarchical structure model is set up, including

Destination layer: adjust object function weight；

Decision-making level: unit output value power consumption；Unit output value blowdown flow rate；The purification difficulty of pollutant；Social status；Geographical position；

Solution layer: improve satisfaction；Reduce power budget；Reduce disposal of pollutants；

(2) structure pairwise comparison matrix: utilized the pairwise comparison matrix of 1～9 dimensional configurations different layers by expert；

(3) calculate single rank order filtering and do consistency check；

(4) calculate total rank order filtering and do consistency check；

(5) weight vector of numerical procedure layer sequence total to destination layer, the i.e. weight of object function

It is then based on multi-objective genetic algorithm optimum for pareto to solve, uses MATLAB to calculate, have invoked in MATLAB Gamultiobj function, concrete step is as follows:

(1) bidding price adjustment cycle n is set；

(2) if estimate to there is AQI value more than 200 in the bidding price adjustment cycle, then start this different pricing of electric power, do not start；Its Middle AQI refers to air quality index, according to the height of air quality index, air quality situation is divided into four grades: good (0-100), light intermediate pollution (101-200), serious pollution (201-300), severe contamination (more than 300), bidding price adjustment coefficient k It is also classified into four grades the most corresponding；

(3) if starting this different pricing of electric power, then bidding price adjustment coefficient k is distributed according to the concrete data of AQI_i, i=1 in the present invention, 2,3,4；

(4) according to customer response model, user's need for electricity amount after bidding price adjustment is calculated；

(5) setting seeks the object function of the best electric price regulation coefficient, including user satisfaction F₁, total need for electricity amount F₂, air matter Need for electricity amount F when volume index is more than 200₃, and the correction weight of three object functions

(6) constraints of optimization, the i.e. bound of bidding price adjustment coefficient k are set；

(7) call gamultiobj function to be optimized and solve, obtain a series of pareto optimal solution, i.e. pareto front end；

(8) bidding price adjustment coefficient k is obtained_iOptimal solution.