CN104616082A - Demand response benefit and potential evaluation method based on electricity price - Google Patents

Abstract

The invention discloses a demand response benefit and potential evaluation method based on electricity price. The method includes the following steps: determining the number of users participating in a demand response project based on the electricity price after m years according to the set user growth rate alpha and the intelligent electricity meter implementation progress beta; calculating electric quantity of each user in unit time one day at the new electricity price according to a user response characteristic model; calculating the average electric quantity at ordinary time of the user at the new electricity price according to the daily electric quantity of the user; predicting the number of the users participating the project from the reference year and evaluating the requirement response benefit and potential of a power plant, a power grid, the users and the environment according to the user response degree. By means of the evaluation, the benefit and the demand response potential of the benefited parties are carefully analyzed after the demand response project based on the electricity price is implemented, the benefit evaluation value is quantized, and the method provides reference for planning and cost recovery of the demand response project, helps the government to make decisions, is favorable for well development of the demand response project in the country, and achieves good social benefit.

Description

A kind of demand response benefit based on electricity price and potential evaluation method

Technical field

The invention belongs to demand Side Management field, particularly based on demand response benefit and the potential evaluation method of electricity price.

Background technology

Demand response is that electrical network supply side provides price or pumping signal; power consumer is impelled to change power mode, few electricity consumption during guiding user peak, Multifunctional electric during low ebb; improve power supplying efficiency, optimize power mode, reach the long-range object of economize energy and protection of the environment.Demand response is divided into based on the demand response of price and demand response two class based on excitation.Demand response based on price refers to that user responds the change of zero potential energy, and correspondingly adjusts need for electricity; Demand response based on excitation refers to that demand response enforcement body is by formulating deterministic or time dependent policy, encourages user when system reliability is affected or electricity price is higher, responds in time and reduction plans.Differ from one another based on excitation and operating based on the demand response of electricity price, research object is the demand response based on electricity price herein.

Demand response project based on electricity price is supported abroad energetically, external enforcement demand response starting comparatively early, market environment is open, the reaction of some pilot projects is good, involved by corresponding evaluation work also has, such as USDOE assesses the benefit of demand response under the electricity market of the whole America.Current, China is carrying forward vigorously the construction of intelligent grid, and the demand response aspect that intelligent grid is important in building just.Rationally whether whether the enforcement of demand response needs a large amount of software and hardware investments, to invest, have enough development potentialities to be the problem that government and electrical network are all paid close attention to.Therefore demand response performance evaluation is carried out, set up effective evaluation system, explore method and the working frame of a standard, the benefit that can produce demand response and potentiality do quantitative assessment, decision-making section will be conducive to and strengthen planning, thus impel the carrying out of demand response project.

At present, the integral system framework of domestic existing demand response performance evaluation, but qualitative examination is more, quantitative test is less, particularly concrete research approach is lacked to the demand response performance evaluation based on electricity price, and existing demand response performance evaluation scheme is after-action review, i.e. project implementation later evaluation.On the basis of the achievement in research of forefathers, the present invention proposes based on the demand response benefit of electricity price and potential evaluation method, the quantitative relation of computation requirement responsiveness and electricity price, before the project implementation, the benefit of each benefited main body of quantitative predication, can provide important references for the design of demand response project and planning.

Find by prior art documents, Chinese Patent Application No. 201210201417.5, denomination of invention: various dimensions demand response comprehensive benefit assessment method, publication number: CN102750656A, the method proposes various dimensions assessment thought, from main body, time and project three dimensions carry out multi dimensional analysis to demand response comprehensive benefit, embody the dynamic change of demand response comprehensive benefit both macro and micro, but the method: (1) can only respond benefit by computation requirement when known users power consumption is cut down, and the specific embodiments prediction power consumption that can not respond according to demand is cut down and carries out performance evaluation, (2) this appraisal procedure does not provide evaluation scheme for concrete demand response measure, just considers the appraisal procedure of demand response comprehensive benefit from structural framing.

Summary of the invention

There is the defect can not carrying out performance evaluation and Potentials in advance for existing demand response comprehensive benefit assessment method, the technical matters that the present invention mainly solves is: for providing a kind of scheme estimated its implementation benefit and potentiality, take into full account project dynamic change based on the demand response project planning of electricity price.

For achieving the above object, the invention provides a kind of demand response benefit based on electricity price and potential evaluation method.

The present invention solves its technical matters by the following technical solutions:

Based on demand response benefit and the potential evaluation method of electricity price, it is characterized in that: comprise the following steps:

The first step is α and intelligent electric meter progress of implementation according to user's rate of growth of setting is β, participates in the demand response project user quantity N' based on electricity price after determining m _mfor;

N' _m＝N _m·β

Wherein, N _mfor the total number of users after m, N _m=n* (1+a) ^m, n is benchmark time every class number of users;

Second step, each user unit interval in odd-numbered day power consumption Q' under calculating new electricity price according to user's response characteristic model _d:

$Q_d^{\′}=Q_d\×{\left(\frac{p_d^{\′}}{p_d}\right)}^{\text{c}}$

In formula, Q _dfor user's unit interval in odd-numbered day power consumption under old electricity price, Q' _dfor user's unit interval in odd-numbered day power consumption under new electricity price; p _dfor former per day electricity price; P _d' be new per day electricity price; C is price elastic coefficient every day.

3rd step, calculates the user average power consumption Q' of section at ordinary times under new electricity price according to user's day power consumption _p:

$Q_p^{\′}=Q_d^{\′}\×T_d/\{\frac{Q_f}{Q_p}\×{(\frac{p_f^{\′}}{p_p^{\′}}/\frac{p_f}{p_p})}^{b1}\×T_f+T_p+\frac{Q_g}{Q_p}\×{(\frac{p_g^{\′}}{p_p^{\′}}/\frac{p_g}{p_p})}^{b2}\×T_g\}$

Q _fit is peak period unit interval power consumption (unit interval is generally per hour) under former electricity price; Q ' _ffor peak period unit interval power consumption under new electricity price; Q _pit is section unit interval power consumption at ordinary times under former electricity price; Q ' _pfor section unit interval power consumption at ordinary times under new electricity price; Q _gfor paddy period unit interval power consumption under former electricity price; Q ' _gfor paddy period unit interval power consumption under new electricity price; B1 is the flat elasticity of substitution coefficient in peak; B2 is the flat elasticity of substitution coefficient of paddy; P _ffor parent peak period electricity price; P ' _ffor new peak period electricity price; P _pfor Yuanping City's period electricity price; P ' _pfor new section electricity price at ordinary times; P _gfor former paddy period electricity price; P ' _gfor Xingu period electricity price; T _dfor day hourage, T _ffor peak period hourage, T _pfor section hourage at ordinary times, T _gfor paddy period hourage;

4th step, calculates user peak period and paddy period average power consumption Q ' under new electricity price according to the average power consumption of section at ordinary times _f, Q ' _g:

$Q_f^{\′}=Q_p^{\′}\×\frac{Q_f}{Q_p}\×{(\frac{p_f^{\′}}{p_p^{\′}}/\frac{p_f}{p_p})}^{b1}$

$Q_g^{\′}=Q_p^{\′}\×\frac{Q_g}{Q_p}\×{(\frac{p_g^{\′}}{p_p^{\′}}/\frac{p_g}{p_p})}^{b2}$

5th step, finally obtains new electricity price lower odd-numbered day peak, flat, paddy period power consumption E ' _f, E ' _p, E' _gand spike day top lotus P ' _ffor:

E′ _f＝Q′ _f×T _f

E' _p＝Q' _p×T _p

E' _g＝Q' _g×T _g

P′ _f＝Q′ _cf×1.25

In formula, Q ' _cffor peak period spike day average power consumption per hour;

6th step, calculates the grid power transmission volume change amount Δ P under new electricity price ₁:

${\mathrm{\ΔP}}_1=\frac{\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}{\mathrm{\ΔP}}_i\mathrm{\σ}}{(1-\mathrm{\λ})(1-\mathrm{\α})}$

ΔP _i＝P _if-P′ _if

Wherein: Δ P _ibe i-th top lotus of participating in demand response project user and reducing, P _iffor this user's standard year top lotus, P ' _iffor this user plans a year top lotus, calculated by the 5th step; N' _mit is the total number of users of m planning year participating in demand response project; σ is user's simultaneity factor; λ is system reserve capacity coefficient;

Calculate the generating capacity reduction of the power plant under new electricity price:

${\mathrm{\ΔP}}_2=\frac{\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}{\mathrm{\ΔP}}_i\mathrm{\σ}}{(1-\mathrm{\λ})(1-\mathrm{\α})(1-\mathrm{\β})}$

Wherein: Δ P _ibe i-th peak load participated in demand response project user and reduce; N' _mit is the total number of users of m planning year participating in demand response project; σ is user's simultaneity factor; λ is system reserve capacity coefficient; α is electrical network distribution loss coefficient; β is power plant's power consumption rate;

To calculate under new electricity price because Unit Commitment frequency reduces the coal consumption reduction Δ B brought:

$\mathrm{\Δ\ξ}=\frac{\stackrel{\‾}{P_2}}{(P^{\max }-\mathrm{\ΔP})}-\frac{\stackrel{\‾}{P_1}}{P^{\max }}$

$E_{\mathrm{DR}}=k\×(\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}^{\′}+E_{\mathrm{ip}}^{\′}+E_{\mathrm{ig}}^{\′})+\underset{i=1}{\overset{N_m-N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}+E_{\mathrm{ip}}+E_{\mathrm{ig}}))$

Wherein: c is the coal consumption of fired power generating unit unit; Δ ξ represents the rate of load condensate implemented demand response and promote, P ^maxfor peak load before enforcement DR; with be respectively average load when having neither part nor lot in and participate in demand response project, by the Q in second step _dwith Q' _dall user's summations are obtained; Δ P is the peak period load cut down after implementing demand response, the correlation factor of rate of load condensate and the coal consumption of coal unit unit e _dRfor user's total electricity consumption after enforcement DR; M is the time limit implementing DR; E ' _ifbe i-th power consumption participating in based on the electricity price demand response project user peak period, E _ifbe i-th power consumption having neither part nor lot in based on the electricity price demand response project user peak period, other the like, k is the number of days of m, is generally 365; ε is the reduction coefficient of user side power consumption to Generation Side.

Implement demand response project m, according to the transmission line capability cost B that described grid power transmission volume change amount is saved _m1for: B _m1=Δ P ₁× θ ₁× (1+ ρ) _m

Wherein: θ ₁for grid side unit can avoid transmission line capability cost; ρ is inflation rate.

Implement demand response project m, according to the distribution capacity cost B that described grid power transmission volume change amount is saved _m2for: B _m2=Δ P ₁× θ ₂× (1+ ρ) _m

In formula, θ ₂for grid side unit can avoid distribution capacity cost;

Operation of power networks cost savings B _m3computing formula is:

$B_{m3}=\frac{\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}{\mathrm{\ΔE}}_i}{(1-l)(1-\mathrm{\α})}\×{\mathrm{\ω}}_1\×{(1+\mathrm{\ρ})}^m$

ΔE _i＝j×(E _if-E′ _if+E _ip-E′ _ip+E _ig-E′ _ig)+k×(E _icf-E′ _icf+E _icp-E′ _icp+E _icg-E′ _icg)

In formula: Δ E _ifor participating in total electricity that demand response project user i saves every year, j is non-spike day quantity, and k is spike day quantity, wherein, and E _ifrepresent non-spike day peak period power consumption, E _icfrepresent peak spike day, power consumption period (obtaining according to above-mentioned steps according to spike period electricity price), the rest may be inferred for other; ω ₁for unit electricity operating cost, unit/kWh; ρ is inflation rate;

Electrical network purchases strategies change B _m4:

$\begin{array}{c}{B}_{m4}=(E\×p_{s0}-k\×(\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}^{\′}\×p_f^{\′}+E_{\mathrm{ip}}^{\′}\×p_p^{\′}+E_{\mathrm{ig}}^{\′}\×p_g^{\′})+\underset{i=1}{\overset{N_m-N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}\×p_f^{\′}+E_{\mathrm{ip}}\×p_p^{\′}+E_{\mathrm{ig}}\×p_g^{\′})))\\ \·{(1+\mathrm{\ρ})}^m\stackrel{\→}{\mathrm{AB}}\end{array}$

In formula: p _s0for implementing the rate for incorporation into the power network before demand response, unit/kWh; E is the user power utilization total amount before implementing demand response, kWh; P' _g, p' _g, p' _gbe respectively new peak, flat, paddy rate for incorporation into the power network, unit/kWh; ρ is inflation rate.

Implement demand response project m, according to the power plants generating electricity Capacity Cost B that described power plants generating electricity volume change amount is saved _m6for:

B _m6＝ΔP ₂×θ×(1+η) ^m

Wherein: θ is that Power Plant Side unit can avoid Capacity Cost; η is Capacity Cost rate of growth;

Unit Commitment cost reduces B _m7for:

B _m7＝p _coalΔB·(1+ρ) ^m

Wherein: p _coalfor power coal price.

Implement demand response project m, energy cost saves B _m10:

$B_{m10}=\underset{i=1}{\overset{3}{\mathrm{\Σ}}}{\mathrm{\θ}}_3^i\×\mathrm{\ϵ}{\×\mathrm{\ΔE}}^{\text{i}}\×{(1+\mathrm{\ρ})}^{\text{m}}$

$\mathrm{\ϵ}=\frac{1}{(1-l)(1-\mathrm{\α})(1-\mathrm{\β})}$

Wherein: (i=1,2,3) be peak, the unit source cost of flat, paddy load stage; Δ E ⁱ(i=1,2,3) be peak, all user power utilization variable quantities of flat, paddy load stage, can be calculated by abovementioned steps; ε is the coefficient of user side electricity reduction to Generation Side; L is terminal power distribution loss coefficient; α is electrical network power transmission and distribution loss coefficients; β is station service power consumption rate; ρ is inflation rate.

Implement demand response project m, demand charge saves B then _m11for:

$\begin{array}{c}{B}_{m11}=(E\×p_0-k\×(\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}^{\′}\×p_f^{\′}+E_{\mathrm{ip}}^{\′}\×p_p^{\′}+E_{\mathrm{ig}}^{\′}\×p_g^{\′})+\underset{i=1}{\overset{N_m-N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}\×p_f^{\′}+E_{\mathrm{ip}}\×p_p^{\′}+E_{\mathrm{ig}}\×p_g^{\′})))\\ \·{(1+\mathrm{\ρ})}^m\end{array}$

In formula: p ₀for implementing the fixing electricity price before demand response, the same (B of other meaning of parameters of unit/kWh _m4and more than);

Implement demand response project m, reliability benefit B _m12for:

$B_{m12}=\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{\mathrm{VOLL}}_i\×{\mathrm{\ΔP}}_i\×T_{\mathrm{TOTAL},i}\×(\mathrm{LOLP}-\mathrm{LO}{\mathrm{LP}}^{\′})\·{(1+\mathrm{\ρ})}^m$

In formula: VOLL _ifor user i Value of lost load, unit/kWh; T _{tOTAL, i}for the T.T. of user i ideal power supply; LOLP is load-loss probability before enforcement demand response; LOLP' is load-loss probability after enforcement demand response; Δ P _ifor the load cut down after user i participation demand response, ρ is inflation rate.

After implementing demand response project m, environmental benefit B _m15for:

B _m15＝(B _13.1+B _13.2)·(1+ρ) ^m

Wherein: B _13.1for implementing the benefit that demand response minimizing mineral fuel use makes Generation Side generate electricity less, the product that the CER equaling the dusty gas such as carbon dioxide, sulphuric dioxide is worth with reduction of discharging:

$\begin{array}{c}{B}_{13.1}=N_{{\mathrm{CO}}_2}\×V_{{\mathrm{CO}}_2}+N_{{\mathrm{SO}}_2}\×V_{{\mathrm{SO}}_2}+N_{{\mathrm{NO}}_x}\×V_{{\mathrm{NO}}_x}\\ ={\mathrm{\ΔE}}_2\×({\mathrm{\σ}}_{{\mathrm{CO}}_2}\×V_{{\mathrm{CO}}_2}+{\mathrm{\σ}}_{{\mathrm{SO}}_2}{\×V}_{{\mathrm{SO}}_2}+{\mathrm{\σ}}_{{\mathrm{NO}}_x}\×V_{{\mathrm{NO}}_x})\end{array}$

In formula: N _cO2, N _sO2, N _nObe respectively carbon dioxide, sulphuric dioxide, oxides of nitrogen CER; V _cO2, V _sO2, V _nOXbe respectively carbon dioxide, sulphuric dioxide, oxides of nitrogen reduction of discharging value; σ _cO2, σ _sO2, σ _nOXbe respectively carbon dioxide, sulphuric dioxide, oxides of nitrogen reduction of discharging coefficient;

B _13.2for implementing the benefit of the peak load shifting that demand response brings, rate of load condensate promotes, and reduces start-stop of generator set frequency, improves generating efficiency:

$B_{13.2}=(E_2-{\mathrm{\ΔE}}_2)\×\mathrm{\Δ\ξ}\×\mathrm{\φ}\×({\mathrm{\σ}}_{{\mathrm{CO}}_2}\×V_{{\mathrm{CO}}_2}+{\mathrm{\σ}}_{{\mathrm{SO}}_2}\×V_{{\mathrm{SO}}_2}+{\mathrm{\σ}}_{{\mathrm{NO}}_x}\×V_{{\mathrm{NO}}_x})/\mathrm{bg}$

In formula: Δ ξ is the rate of load condensate percentage point implementing demand response lifting; Bg is for needing coal unit net coal consumption rate, g/kWh; for the correlation factor of rate of load condensate and the coal consumption of coal unit unit; E ₂for reduction before enforcement demand response is to the electricity of Generation Side.

Beneficial effect of the present invention is mainly:

(1) elasticity of demand estimating user response quautity is utilized.Only need the statistical estimation of user's history electricity consumption data and elasticity of demand, just according to the response quautity of electricity price scheme estimating user, the theoretical foundation of science can be had.

(2) the demand response performance evaluation scheme of complete set is provided.According to power consumer standard year electricity consumption data and demand response electricity price model measuring user response quautity (i.e. peak clipping amount, load transfer plan amount), response quautity is utilized to estimate planning year demand response benefit and potentiality, the process being implemented into demand response recruitment evaluation of what scheme was complete cover demand response.

(3) can be applied in demand response project planning.Achieve the advance evaluation to demand response benefit, namely assess before the demand response project implementation, effectively solve other evaluation measures and can only assess this problem according to result after the project implementation, thus important guiding can be provided for demand response project planning.

Accompanying drawing explanation

Fig. 1 is performance evaluation structural drawing of the present invention.

Embodiment

The appraisal procedure that the present invention proposes analyzes meticulously to be implemented based on benefit of benefited main body each after the demand response project of electricity price and the potentiality of demand response, quantize benefit valuation, can be the recovery of demand response project cost and reference is provided, help government department's decision-making, contribute to demand response project better to launch at home, to realize good social benefit.

The concrete implementation step of technical scheme that the present invention solves its technical matters is as follows:

The first step, determines to participate in the information based on the demand response project user of electricity price.Choose the total number of users in certain benchmark time, type, all types of user day part (being divided into peak, flat, paddy period) power consumption information, calculate the history rate of growth of number of users, investigation user's smart machine (intelligent electric meter) installation situation and user participate in the wish of demand response project, and predict the number of users after forthcoming years by benchmark time number of users and number of users history rate of growth, and then by user's smart machine installation situation and the number of users participating in demand response project after participating in demand response project wish prediction forthcoming years;

If the benchmark time, every class number of users was n, user's rate of growth is α, then the total number of users N after m _mfor:

N _m＝n*(1+a) ^m(1)

Wherein, intelligent electric meter progress of implementation is β, then participate in the number of users N' of demand response project after m _mfor:

N' _m＝N _m·β (2)

Second step, utilize known Demand Elasticity Coefficient (can be calculated by history power information) and user power utilization information architecture user response characteristic model, the Calculation Basis time implements the demand response degree of dissimilar unique user after tou power price and Critical Peak Pricing project, i.e. electricity consumption variable quantity, comprises day part electricity consumption variable quantity and daily Electrical change amount.User's response characteristic model comprises two aspects: utilize day price elasticity to estimate the change of user's total electricity consumption every day after carrying out new tou power price or Critical Peak Pricing; The peak valley elasticity of substitution is utilized to estimate to carry out the change of peak interval of time power consumption after new tou power price or Critical Peak Pricing.

Wherein in second step, user's response characteristic model is determined by following formula:

After carrying out new electricity price, the per day power consumption computation model of user:

$Q_d^{\′}=Q_d\×{\left(\frac{p_d^{\′}}{p_d}\right)}^{\text{c}}---\left(3\right)$

In formula, Q _dfor user's unit interval in odd-numbered day power consumption under old electricity price, Q' _dfor user's unit interval in odd-numbered day power consumption under new electricity price; p _dfor former per day electricity price; P ' _dfor new per day electricity price; C is price elastic coefficient every day.

The average power consumption Q' of section is at ordinary times calculated under new electricity price according to user's day power consumption _p:

$Q_p^{\′}=Q_d^{\′}\×T_d/\{\frac{Q_f}{Q_p}\×{(\frac{p_f^{\′}}{p_p^{\′}}/\frac{p_f}{p_p})}^{b1}\×T_f+T_p+\frac{Q_g}{Q_p}\×{(\frac{p_g^{\′}}{p_p^{\′}}/\frac{p_g}{p_p})}^{b2}\×T_g\}---\left(4\right)$

Q _fit is peak period unit interval power consumption (unit interval is generally per hour) under former electricity price; Q ' _ffor peak period unit interval power consumption under new electricity price; Q _pit is section unit interval power consumption at ordinary times under former electricity price; Q' _pfor section unit interval power consumption at ordinary times under new electricity price; Q _gfor paddy period unit interval power consumption under former electricity price; Q ' _gfor paddy period unit interval power consumption under new electricity price; B1 is the flat elasticity of substitution coefficient in peak; B2 is the flat elasticity of substitution coefficient of paddy; P _ffor parent peak period electricity price; P ' _ffor new peak period electricity price; P _pfor Yuanping City's period electricity price; P ' _pfor new section electricity price at ordinary times; P _gfor former paddy period electricity price; P ' _gfor Xingu period electricity price; T _dfor day hourage, T _ffor peak period hourage, T _pfor section hourage at ordinary times, T _gfor paddy period hourage.

Peak period and paddy period average power consumption Q ' under new electricity price is calculated according to the average power consumption of section at ordinary times _f, Q ' _g:

$Q_f^{\′}=Q_p^{\′}\×\frac{Q_f}{Q_p}\×{(\frac{p_f^{\′}}{p_p^{\′}}/\frac{p_f}{p_p})}^{b1}---\left(5\right)$

$Q_g^{\′}=Q_p^{\′}\×\frac{Q_g}{Q_p}\×{(\frac{p_g^{\′}}{p_p^{\′}}/\frac{p_g}{p_p})}^{b2}---\left(6\right)$

Finally obtain new electricity price lower odd-numbered day peak, flat, paddy period power consumption E ' _f, E' _p, E' _gand spike day top lotus P ' _ffor:

E′ _f＝Q′ _f×T _f(7)

E' _p＝Q' _p×T _p(8)

E' _g＝Q' _g×T _g(9)

P′ _f＝Q′ _cf×1.25 (10)

In formula, Q ' _cffor peak period spike day average power consumption per hour.Different and different according to electricity price of the power consumption of non-spike day and spike day.

3rd step, participate in the quantity of demand response project user prediction planning year, the unique user electricity consumption variable quantity utilizing second step to obtain obtains planning year user power utilization variable quantity, finally assesses power plant, electrical network, the demand response benefit of user and environment and potentiality respectively.Power plant aspect, performance evaluation is divided into: Capacity Cost saving, energy cost saving, Unit Commitment cost savings, power plant's power selling income change, generator marginal cost reduce the cost saved; Electrical network aspect, the change of transmission line capability cost savings, distribution capacity cost savings, operation of power networks cost savings, electrical network purchases strategies, the change of electrical network power selling income; Customer-side, mainly contains demand charge saving, user dependability benefit; Society aspect, performance evaluation is mainly the assessment of environmental benefit, be specially, owing to implementing demand response, Generation Side is generated electricity less, reduce mineral fuel to use, two is the effects owing to implementing the peak load shifting that demand response brings, and rate of load condensate promotes, reduce start-stop of generator set frequency, improve generating efficiency.

Wherein in the 3rd step, the performance evaluation method of electrical network, power plant, user and society is:

Electrical network aspect, benefit is mainly divided into: the change of transmission line capability cost savings, distribution capacity cost savings, operation of power networks cost savings, electrical network purchases strategies, the change of electrical network power selling income.

Transmission line capability cost savings, refer to owing to implementing demand response, and the power transmission network equipment investment reduced, implement the transmission line capability cost savings B after demand response project m _m1:

B _m1＝ΔP ₁×θ ₁×(1+ρ) ^m(11)

${\mathrm{\ΔP}}_1=\frac{\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}{\mathrm{\ΔP}}_i\mathrm{\σ}}{(1-\mathrm{\λ})(1-\mathrm{\α})}---\left(12\right)$

ΔP _i＝P _if-P′ _if(13)

Wherein: Δ P _ibe i-th top lotus of participating in demand response project user and reducing, P _iffor this user's standard year top lotus, P ' _iffor this user plans a year top lotus, calculated by the 5th step; N' _mit is the total number of users of m planning year participating in demand response project; σ is user's simultaneity factor; λ is system reserve capacity coefficient; θ ₁for grid side unit can avoid transmission line capability cost; ρ is inflation rate.

Distribution capacity cost savings, refer to owing to implementing demand response, and the Distribution Network Equipment investment reduced.

Project implementation m distribution capacity cost savings B _m2computing formula is:

B _m2＝ΔP ₁×θ ₂×(1+ρ) ^m(14)

${\mathrm{\ΔP}}_2=\frac{\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}{\mathrm{\ΔP}}_i\mathrm{\σ}}{(1-\mathrm{\λ})(1-\mathrm{\α})(1-\mathrm{\β})}---\left(15\right)$

In formula, Δ P _ibe i-th peak load participated in demand response project user and reduce; N' _mit is the total number of users of m planning year participating in demand response project; σ is user's simultaneity factor; λ is system reserve capacity coefficient; α is electrical network distribution loss coefficient; β is power plant's power consumption rate; θ is that Power Plant Side unit can avoid Capacity Cost; η is Capacity Cost rate of growth; Other meaning of parameters are the same.

Operation of power networks cost savings, refer to because of demand response measure, the running cost that power grid enterprises reduce.

Project implementation m operation of power networks cost savings B _m3computing formula is:

$B_{m3}=\frac{\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}{\mathrm{\ΔE}}_i}{(1-l)(1-\mathrm{\α})}\×{\mathrm{\ω}}_1\×{(1+\mathrm{\ρ})}^m---\left(16\right)$

ΔE _i＝j×(E _if-E′ _if+E _ip-E′ _ip+E _ig-E′ _ig)+k×(E _icf-E′ _icf+E _icp-E′ _icp+E _icg-E′ _icg) (17)

In formula: Δ E _ifor participating in total electricity that demand response project user i saves every year, j is non-spike day quantity, and k is spike day quantity, wherein, and E _ifrepresent non-spike day peak period power consumption, E _icfrepresent peak spike day, power consumption period (obtaining according to above-mentioned steps according to spike period electricity price), the rest may be inferred for other; ω ₁for unit electricity operating cost, unit/kWh; ρ is inflation rate.

Electrical network purchases strategies changes, by the user power utilization amount implemented before and after peak, flat, paddy electricity price and the price decision of electrical network power purchase.Electrical network purchases strategies changes, and refers to implement the demand response project based on price, and user abandons or shift the load electricity consumption of high rate period, and power consumption changes, and the cost that electrical network buys electric energy from Power Plant Side also changes.Project implementation m electrical network purchases strategies change B _m4:

$\begin{array}{c}{B}_{m4}=(E\×p_{s0}-k\×(\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}^{\′}\×p_f^{\′}+E_{\mathrm{ip}}^{\′}\×p_p^{\′}+E_{\mathrm{ig}}^{\′}\×p_g^{\′})+\underset{i=1}{\overset{N_m-N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}\×p_f^{\′}+E_{\mathrm{ip}}\×p_p^{\′}+E_{\mathrm{ig}}\×p_g^{\′})))\\ \·{(1+\mathrm{\ρ})}^m\stackrel{\→}{\mathrm{AB}}\end{array}---\left(18\right)$

In formula: p _s0for implementing the rate for incorporation into the power network before demand response, unit/kWh; E is the user power utilization total amount before implementing demand response, kWh; K is m number of days; P' _g, p' _g, p' _gbe respectively new peak, flat, paddy rate for incorporation into the power network, unit/kWh; ρ is inflation rate.

Electrical network power selling income changes, by the user power utilization amount implemented before and after new electricity price and electrical network sale of electricity price decision.Electrical network power selling income change B _m5: (wherein B _m11for demand charge is saved)

B _m5＝-B _m11(19)

Power plant aspect, performance evaluation is divided into: Capacity Cost saving, energy cost saving, Unit Commitment cost savings, power plant's power selling income change, generator marginal cost reduce the cost saved.

Capacity Cost is saved and is referred to the investment cost that enterprise of power plant reduces owing to can avoid capacity, and project implementation m Capacity Cost saves B _m6for:

B _m6＝ΔP ₂×θ×(1+η) ^m(20)

Wherein: θ is that Power Plant Side unit can avoid Capacity Cost; η is Capacity Cost rate of growth; Other parameters are the same.

Unit Commitment cost reduces.The remarkable result implementing DR improves rate of load condensate, mild load curve exactly.Here the correlation factor of rate of load condensate and the coal consumption of coal unit unit is introduced (represent that rate of load condensate often promotes 1 percentage point, the coal consumption of coal unit unit declines ).Owing to decreasing Unit Commitment frequency, start-up and shut-down costs reduces.Power coal price is p _coal, c is the coal consumption of fired power generating unit unit, and after enforcement DR, user's total electricity consumption is E _dR, ε is the reduction coefficient of user side power consumption to Generation Side.The Unit Commitment cost of project implementation m reduces B _m7for:

$\mathrm{\Δ\ξ}=\frac{\stackrel{\‾}{P_2}}{(P^{\max }-\mathrm{\ΔP})}-\frac{\stackrel{\‾}{P_1}}{P^{\max }}---\left(22\right)$

$E_{\mathrm{DR}}=k\×(\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}^{\′}+E_{\mathrm{ip}}^{\′}+E_{\mathrm{ig}}^{\′})+\underset{i=1}{\overset{N_m-N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}+E_{\mathrm{ip}}+E_{\mathrm{ig}}))---\left(23\right)$

Wherein: c is the coal consumption of fired power generating unit unit; Δ ξ represents the rate of load condensate implemented demand response and promote, P ^maxfor peak load before enforcement DR; with be respectively average load when having neither part nor lot in and participate in demand response project, by the Q in second step _dwith Q' _dall user's summations are obtained; Δ P is the peak period load cut down after implementing demand response, the correlation factor of rate of load condensate and the coal consumption of coal unit unit e _dRfor user's total electricity consumption after enforcement DR; M is the time limit implementing DR; E ' _ifbe i-th power consumption participating in based on the electricity price demand response project user peak period, E _ifbe i-th power consumption having neither part nor lot in based on the electricity price demand response project user peak period, other the like, k is the number of days of m, is generally 365; ε is the reduction coefficient of user side power consumption to Generation Side.。

Power plant's sale of electricity change, changes identical with electrical network purchases strategies.

B _m8＝-B _m4(24)

Genset marginal cost reduces the production cost saved, and namely the spike period can avoid marginal cost and benefit.After implementing this project, the computing formula of m is:

B _m9＝(E _cf·(1+δ) ^m-E′ _cf)×(P _f-P′ _f)÷P _f×α×LMP×100×(1+ρ) ^m(25)

In formula, the Critical Peak Pricing that the minimizing that α is 1% peakload brings reduces number percent; LMP is the average marginal cost price of spike period under former electricity price; E _cffor spike period power consumption under former electricity price; E ' _cffor spike period demand response amount; P _ffor spike period peak power under former electricity price; P ' _ffor spike period demand response peak power; δ is spike pilot power demand annual growth; ρ is inflation rate.

Energy cost is saved, namely because generated energy reduces, and the energy investment that generating plant reduces.Determined by the energy cost of the unit quantity of electricity of Different periods and the electricity consumption variable quantity of Different periods user.The energy cost implementing m saves B _m10:

$B_{m10}=\underset{i=1}{\overset{3}{\mathrm{\Σ}}}{\mathrm{\θ}}_3^i\×\mathrm{\ϵ}{\×\mathrm{\ΔE}}^{\text{i}}\×{(1+\mathrm{\ρ})}^{\text{m}}---\left(26\right)$

$\mathrm{\ϵ}=\frac{1}{(1-l)(1-\mathrm{\α})(1-\mathrm{\β})}---\left(27\right)$

Wherein: (i=1,2,3) be peak, the unit source cost of flat, paddy load stage; Δ E ⁱ(i=1,2,3) be peak, all user power utilization variable quantities of flat, paddy load stage, can be calculated by abovementioned steps; ε is the coefficient of user side electricity reduction to Generation Side.L is terminal power distribution loss coefficient; α is electrical network power transmission and distribution loss coefficients; β is station service power consumption rate; ρ is inflation rate.

Customer-side, mainly contains: demand charge is saved, i.e. electrical network power selling income change; User dependability benefit.

Demand charge is saved, and refers to that user participates in implementing the demand response project based on price, the minimizing of the electric cost expenditure that the load electricity consumption abandoning or shift high rate period brings.

Project implementation m demand charge saves B _m11computing formula be:

$\begin{array}{c}{B}_{m11}=(E\×p_0-k\×(\underset{i=1}{\overset{N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}^{\′}\×p_f^{\′}+E_{\mathrm{ip}}^{\′}\×p_p^{\′}+E_{\mathrm{ig}}^{\′}\×p_g^{\′})+\underset{i=1}{\overset{N_m-N_m^{\′}}{\mathrm{\Σ}}}(E_{\mathrm{if}}\×p_f^{\′}+E_{\mathrm{ip}}\×p_p^{\′}+E_{\mathrm{ig}}\×p_g^{\′})))\\ \·{(1+\mathrm{\ρ})}^m\end{array}----\left(\text{28}\right)$

In formula: p ₀for implementing the fixing electricity price before demand response, unit/kWh; E is before implementing demand response, user power utilization amount, kWh; P' _f, p' _p, p' _gfor new peak, flat, paddy electricity price, unit/kWh; E ' _f, E' _p, E ' _gafter demand response, the power consumption of Yong Hufeng, flat, paddy period; ρ is inflation rate.

User dependability benefit, demand response project implementation is equivalent to increase system cloud gray model margin capacity in the load peak period, reduces user and to have a power failure probability, improve power supply reliability.

The reliability benefit B of project implementation m _m12all adopt following expression:

$B_{m12}=\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{\mathrm{VOLL}}_i\×{\mathrm{\ΔP}}_i\×T_{\mathrm{TOTAL},i}\×(\mathrm{LOLP}-\mathrm{LO}{\mathrm{LP}}^{\′})\·{(1+\mathrm{\ρ})}^m---\left(29\right)$

In formula: VOLL _ifor user i Value of lost load, unit/kWh; T _{tOTAL, i}for the T.T. of user i ideal power supply; LOLP is load-loss probability before enforcement demand response; LOLP' is load-loss probability after enforcement demand response; Δ P _ifor the load cut down after user i participation demand response, as previously mentioned; ρ is inflation rate.

Society aspect, performance evaluation is mainly the assessment of environmental benefit.

Environmental benefit is made up of two parts, and one is benefit B Generation Side being generated electricity less owing to implementing demand response to reduce mineral fuel use _13.1, the product that the CER equaling the dusty gas such as carbon dioxide, sulphuric dioxide is worth with reduction of discharging.

$\left(30\right)---\begin{array}{c}{B}_{13.1}=N_{{\mathrm{CO}}_2}\×V_{{\mathrm{CO}}_2}+N_{{\mathrm{SO}}_2}\×V_{{\mathrm{SO}}_2}+N_{{\mathrm{NO}}_x}\×V_{{\mathrm{NO}}_x}\\ ={\mathrm{\ΔE}}_2\×({\mathrm{\σ}}_{{\mathrm{CO}}_2}\×V_{{\mathrm{CO}}_2}+{\mathrm{\σ}}_{{\mathrm{SO}}_2}{\×V}_{{\mathrm{SO}}_2}+{\mathrm{\σ}}_{{\mathrm{NO}}_x}\×V_{{\mathrm{NO}}_x})\end{array}$

In formula: N _cO2, N _sO2, N _nObe respectively carbon dioxide, sulphuric dioxide, oxides of nitrogen CER; V _cO2, V _sO2, V _nOXbe respectively carbon dioxide, sulphuric dioxide, oxides of nitrogen reduction of discharging value; σ _cO2, σ _sO2, σ _nOXbe respectively carbon dioxide, sulphuric dioxide, oxides of nitrogen reduction of discharging coefficient.

Two is the benefit B owing to implementing the peak load shifting that demand response brings _13.2, rate of load condensate promotes, and reduces start-stop of generator set frequency, improves generating efficiency.

$B_{13.2}=(E_2-{\mathrm{\ΔE}}_2)\×\mathrm{\Δ\ξ}\×\mathrm{\φ}\×({\mathrm{\σ}}_{{\mathrm{CO}}_2}\×V_{{\mathrm{CO}}_2}+{\mathrm{\σ}}_{{\mathrm{SO}}_2}\×V_{{\mathrm{SO}}_2}+{\mathrm{\σ}}_{{\mathrm{NO}}_x}\×V_{{\mathrm{NO}}_x})/\mathrm{bg}---\left(31\right)$

In formula: Δ ξ is the rate of load condensate percentage point implementing demand response lifting; Bg is for needing coal unit net coal consumption rate, g/kWh; for the correlation factor of rate of load condensate and the coal consumption of coal unit unit, represent that rate of load condensate often promotes 1 percentage point, the coal consumption of coal unit unit declines e ₂for reduction before enforcement demand response is to the electricity of Generation Side.

Take into account inflation rate, project implementation m, total environmental benefit B _m15for:

B _m15＝(B _13.1+B _13.2)·(1+ρ) ^m(32)

Demand response potential evaluation method of the present invention, consider the dynamic change of project, count the impact of inflation, power demand annual growth, intelligent metering framework (AMI) popularity rate and demand response user rate of growth, calculate the demand response benefit growth pattern in planning year.

Demand response performance evaluation is divided into four aspects.Electrical network aspect, performance evaluation has the change of transmission line capability cost savings, distribution capacity cost savings, operation of power networks cost savings, electrical network purchases strategies, the change of electrical network power selling income; Power plant aspect, performance evaluation has Capacity Cost saving, energy cost saving, Unit Commitment cost savings, power plant's power selling income change, generator marginal cost to reduce the cost saved; Customer-side, performance evaluation has electricity cost to save and reliability benefit; Society aspect, the environmental benefit of evaluation requirement response.

The demand response benefit based on electricity price propose the present invention and potential evaluation method are applied in the demand response project benefits evaluation process based on tou power price.Embodiment is as follows:

Somewhere Utilities Electric Co. intends the pilot project of carrying out a tou power price and Critical Peak Pricing, needs to estimate this Project Benefit, to determine period of cost recovery in project planning.

User situation.One class user, is equipped with the equipment such as air-conditioning and intelligent electric meter; Two class users, are equipped with air-conditioning; Three class users, domestic consumer's (non-using air-condition and intelligent electric meter).Within 2013, total number of users is 10000, and wherein the number percent of a class, two classes, three class users is respectively: 30%, 40%, 30%.Number of users rate of growth 1%.

Project details.According to project planning, the user's ratio that intelligent electric meter is housed setting 2013-2016 years annual end of the year is respectively 15%, 60%, 100%, 100%; Within 2013-2017 years, have the progress that intelligent electric meter user participates in demand response project and be respectively 10%, 25%, 30%, 25%, 10%; Project intends four months (7-10 month) of enforcement, at 12 days spike day, at 110 days non-spike day, and the peak of every day, flat, paddy hourage are respectively 8,7,9 (hour); Wherein when spike daily peak load and spike day peak, the ratio of average load is 1.25.The load condition of this area's history within appointment month is as follows:

Before implementing demand response project, initial electricity price is 0.55 yuan/kWh.Project implementation phase, electricity price 0.9 yuan/kWh during spike day peak, at ordinary times electricity price 0.52 yuan/kWh, electricity price 0.21 yuan/kWh during paddy; Electricity price 0.8 yuan/kWh during non-spike day peak, at ordinary times electricity price 0.58 yuan/kWh, electricity price 0.26 yuan/kWh during paddy.Power plant's rate for incorporation into the power network, 0.37 yuan/KWh.

The conventional estimated value of elasticity of demand of somewhere pilot project:

Performance evaluation result:

(1) customer-side.Project implementation phase, a class, two classes, three class unique user 2013-2027 electricity bills are saved and are respectively: 530.72 yuan, 341.89 yuan, 51.63 yuan; 390.81,197.80,97.82. reliability benefit present worth:

(2) electrical network aspect.Project implementation phase, 2013-2027 years, total purchases strategies reduced 34692.4 yuan, power selling income reduces 2701246.6 yuan, and operation cost saves 41.672.6 unit, and Transmission Cost saves 14876182.4 yuan, distribution cost saves 9070842.9 yuan, amounts to benefit 21322143.74 yuan.

(3) power plant aspect.Project implementation phase, within 2013-2027 years, total Capacity Cost saves 22215649.8 yuan, energy cost saves 2058147.7 yuan, Unit Commitment cost savings 7272481.4 yuan, power selling income reduces by 747451.6 yuan, marginal price reduces the cost savings 1808390.8 yuan brought, and amounts to benefit 32607218.03 yuan.

(4) social aspect.2013-2027 years, the environmental benefit 800718.7 yuan that a class user produces, two class users-55033.4 yuan, three class users 888005.5 yuan, amounted to 1633690.8 yuan.

Claims (8)

1., based on demand response benefit and the potential evaluation method of electricity price, it is characterized in that: comprise the following steps:

The first step is α and intelligent electric meter progress of implementation according to user's rate of growth of setting is β, participates in the demand response project user quantity N' based on electricity price after determining m _mfor;

N' _m＝N _m·β

Wherein, N _mfor the total number of users after m, N _m=n* (1+a) ^m, n is benchmark time every class number of users;

Second step, each user unit interval in odd-numbered day power consumption Q' under calculating new electricity price according to user's response characteristic model _d:

In formula, Q _dfor user's unit interval in odd-numbered day power consumption under old electricity price, Q' _dfor user's unit interval in odd-numbered day power consumption under new electricity price; p _dfor former per day electricity price; P ' _dfor new per day electricity price; C is price elastic coefficient every day.

3rd step, calculates the user average power consumption Q' of section at ordinary times under new electricity price according to user's day power consumption _p:

Q _fit is peak period unit interval power consumption (unit interval is generally per hour) under former electricity price; Q ' _ffor peak period unit interval power consumption under new electricity price; Q _pit is section unit interval power consumption at ordinary times under former electricity price; Q' _pfor section unit interval power consumption at ordinary times under new electricity price; Q _gfor paddy period unit interval power consumption under former electricity price; Q' _gfor paddy period unit interval power consumption under new electricity price; B1 is the flat elasticity of substitution coefficient in peak; B2 is the flat elasticity of substitution coefficient of paddy; P _ffor parent peak period electricity price; P ' _ffor new peak period electricity price; P _pfor Yuanping City's period electricity price; P ' _pfor new section electricity price at ordinary times; P _gfor former paddy period electricity price; P ' _gfor Xingu period electricity price; T _dfor day hourage, T _ffor peak period hourage, T _pfor section hourage at ordinary times, T _gfor paddy period hourage;

4th step, calculates user peak period and paddy period average power consumption Q ' under new electricity price according to the average power consumption of section at ordinary times _f, Q ' _g:

5th step, finally obtains new electricity price lower odd-numbered day peak, flat, paddy period power consumption E ' _f, E' _p, E' _gand spike day top lotus P ' _ffor:

E′ _f＝Q′ _f×T _f

E' _p＝Q' _p×T _p

E' _g＝Q' _g×T _g

P′ _f＝Q′ _cf×1.25

In formula, Q ' _cffor peak period spike day average power consumption per hour;

6th step, calculates the grid power transmission volume change amount Δ P under new electricity price ₁:

ΔP _i＝P _if-P′ _if

Wherein: Δ P _ibe i-th top lotus of participating in demand response project user and reducing, P _iffor this user's standard year top lotus, P ' _iffor this user plans a year top lotus, calculated by the 5th step; N' _mit is the total number of users of m planning year participating in demand response project; σ is user's simultaneity factor; λ is system reserve capacity coefficient;

Calculate the generating capacity reduction of the power plant under new electricity price:

Wherein: Δ P _ibe i-th peak load participated in demand response project user and reduce; N' _mit is the total number of users of m planning year participating in demand response project; σ is user's simultaneity factor; λ is system reserve capacity coefficient; α is electrical network distribution loss coefficient; β is power plant's power consumption rate;

To calculate under new electricity price because Unit Commitment frequency reduces the coal consumption reduction Δ B brought:

Wherein: c is the coal consumption of fired power generating unit unit; Δ ξ represents the rate of load condensate implemented demand response and promote, P ^maxfor peak load before enforcement DR; with be respectively average load when having neither part nor lot in and participate in demand response project, by the Q in second step _dwith Q' _dall user's summations are obtained; Δ P is the peak period load cut down after implementing demand response, the correlation factor of rate of load condensate and the coal consumption of coal unit unit e _dRfor user's total electricity consumption after enforcement DR; M is the time limit implementing DR; E ' _ifbe i-th power consumption participating in based on the electricity price demand response project user peak period, E _ifbe i-th power consumption having neither part nor lot in based on the electricity price demand response project user peak period, other the like, k is the number of days of m, is generally 365; ε is the reduction coefficient of user side power consumption to Generation Side.

2. the demand response benefit based on electricity price according to claim 1 and potential evaluation method, is characterized in that: implement demand response project m, according to the transmission line capability cost B that described grid power transmission volume change amount is saved _m1for: B _m1=Δ P ₁× θ ₁× (1+ ρ) ^m

Wherein: θ ₁for grid side unit can avoid transmission line capability cost; ρ is inflation rate.

3. the demand response benefit based on electricity price according to claim 2 and potential evaluation method, is characterized in that: implement demand response project m, according to the distribution capacity cost B that described grid power transmission volume change amount is saved _m2for: B _m2=Δ P ₁× θ ₂× (1+ ρ) ^m

In formula, θ ₂for grid side unit can avoid distribution capacity cost;

Operation of power networks cost savings B _m3computing formula is:

ΔE _i＝j×(E _if-E′ _if+E _ip-Eθ _ip+E _ig-Eθ _ig)+k×(E _icf-E′ _icf+E _icp-E′ _icp+E _icg-E′ _icg)

In formula: Δ E _ifor participating in total electricity that demand response project user i saves every year, j is non-spike day quantity, and k is spike day quantity, wherein, and E _ifrepresent non-spike day peak period power consumption, E _icfrepresent peak spike day, power consumption period (obtaining according to above-mentioned steps according to spike period electricity price), the rest may be inferred for other; ω ₁for unit electricity operating cost, unit/kWh; ρ is inflation rate;

Electrical network purchases strategies change B _m4:

In formula: p _s0for implementing the rate for incorporation into the power network before demand response, unit/kWh; E is the user power utilization total amount before implementing demand response, kWh; P' _g, p' _g, p' _gbe respectively new peak, flat, paddy rate for incorporation into the power network, unit/kWh; ρ is inflation rate.

4. the demand response benefit based on electricity price according to claim 3 and potential evaluation method, is characterized in that: implement demand response project m, according to the power plants generating electricity Capacity Cost B that described power plants generating electricity volume change amount is saved _m6for:

B _m6＝ΔP ₂×θ×(1+η) ^m

Wherein: θ is that Power Plant Side unit can avoid Capacity Cost; η is Capacity Cost rate of growth;

Unit Commitment cost reduces B _m7for:

B _m7＝p _coalΔB·(1+ρ) ^m

Wherein: p _coalfor power coal price.

5. the demand response benefit based on electricity price according to claim 4 and potential evaluation method, is characterized in that: implement demand response project m, energy cost saves B _m10:

Wherein: for the unit source cost of peak, flat, paddy load stage; Δ E ⁱ(i=1,2,3) be peak, all user power utilization variable quantities of flat, paddy load stage, can be calculated by abovementioned steps; ε is the coefficient of user side electricity reduction to Generation Side; L is terminal power distribution loss coefficient; α is electrical network power transmission and distribution loss coefficients; β is station service power consumption rate; ρ is inflation rate.

6. the demand response benefit based on electricity price according to claim 5 and potential evaluation method, is characterized in that: implement demand response project m, demand charge saves B then _m11for:

In formula: p ₀for implementing the fixing electricity price before demand response, the same (B of other meaning of parameters of unit/kWh _m4and more than).

7. the demand response benefit based on electricity price according to claim 6 and potential evaluation method, is characterized in that: implement demand response project m, reliability benefit B _m12for:

In formula: VOLL _ifor user i Value of lost load, unit/kWh; T _{tOTAL, i}for the T.T. of user i ideal power supply; LOLP is load-loss probability before enforcement demand response; LOLP' is load-loss probability after enforcement demand response; Δ P _ifor the load cut down after user i participation demand response, ρ is inflation rate.

8. the demand response benefit based on electricity price according to claim 7 and potential evaluation method, is characterized in that: implement demand response item order m, environmental benefit B _m15for:

B _m15＝(B _13.1+B _13.2)·(1+ρ) ^m

Wherein: B _13.1for implementing the benefit that demand response minimizing mineral fuel use makes Generation Side generate electricity less, the product that the CER equaling the dusty gas such as carbon dioxide, sulphuric dioxide is worth with reduction of discharging:

In formula: N _cO2, N _sO2, N _nObe respectively carbon dioxide, sulphuric dioxide, oxides of nitrogen CER; V _cO2, V _sO2, V _nOXbe respectively carbon dioxide, sulphuric dioxide, oxides of nitrogen reduction of discharging value; σ _cO2, σ _sO2, σ _nOXbe respectively carbon dioxide, sulphuric dioxide, oxides of nitrogen reduction of discharging coefficient;

B _13.2for implementing the benefit of the peak load shifting that demand response brings, rate of load condensate promotes, and reduces start-stop of generator set frequency, improves generating efficiency:

In formula: Δ ξ is the rate of load condensate percentage point implementing demand response lifting; Bg is for needing coal unit net coal consumption rate, g/kWh; for the correlation factor of rate of load condensate and the coal consumption of coal unit unit; E ₂for reduction before enforcement demand response is to the electricity of Generation Side.