CN103955868A - Demand response effect evaluation method based on fuzzy comprehensive analysis - Google Patents

Abstract

The invention discloses a demand response effect evaluation method based on fuzzy comprehensive analysis. Based on power grids and social demands, reliability, economical efficiency and environmental protection are used as three indexes for measuring a demand response effect. According to the reliability index, the participation way of a use, response time and the reliability degree of response capacity are mainly measured in the demand response process. According to the economical efficiency index, the cost of the unit response capacity for carrying out a demand response and the reduction of the power grid valley-to-peak difference due to a demand response event are mainly measured. According to the environmental protection index, after the demand response is carried out, measurement is mainly carried out from the environmental pollutant discharge quantity and the user energy consumption level. In the index calculation process, the indexes difficult to quantize are expressed in a relative value or level mode, and understanding and calculation are easy. In the index solution process, a fuzzy comprehensive analysis method is adopted, the problems that the evaluation indexes are fuzzy and difficult to quantize can be solved well, and the method is suitable for different demand response stages of the different power grids.

Description

A kind of demand response effect evaluation method based on Fuzzy Analysis

Technical field

The invention belongs to electricity consumption service technology field, be specifically related to a kind of demand response effect evaluation method based on Fuzzy Analysis.

Background technology

Intelligent grid is by emerging IT technology such as Internet of Things, cloud computing, data minings, by intelligence measure, the technology such as efficient control, high-speed communication, manage neatly, dispatch, integrate Demand-side resource, demand Side Management just draws the load that the noisy mode of rationing the power supply changes dependence power electronics and the communication technology into shift control, interruptible load control and maximum demand control from the original pressure of dependence merely, and demand response is just becoming the study hotspot of dsm.Along with the continuous propelling that dsm experimental city is built, under the effect of difference excitation, Price Mechanisms, demand response is just entering practical stage.

Because of Demand-side resource have type complexity, characteristic different, control the features such as various, in event, being prone to response instruction is difficult to promptly and accurately receive, fails to meet ideal response capacity, response speed and fail to meet the problems such as scheduling requirement, cause actual result and expected results gap larger, this will bring very big challenge to power grid security, stable, operation.Be analogous to Generation Side resource, reliability, economy and the feature of environmental protection how to weigh Demand-side resource become the difficult problem that electrical network faces.

The target of demand response recruitment evaluation is from electrical network angle, and the reduction curve producing under the effect of Demand-side aggregation of resources is carried out to unified, comprehensively evaluation, and its result directly affects electrical network, user's interests, even can affect the future development of demand response.Therefore, build demand response recruitment evaluation system and become the difficult point that solution is needed in demand response scale application badly.

A lot of research at present concentrates on aspects such as how evaluating user's request response potentiality, incentive mechanism, Interactive Mechanism, less to the research of demand response recruitment evaluation index and system.

Summary of the invention

The invention provides a kind of demand response effect evaluation method based on Fuzzy Analysis, base oneself upon electrical network, social demand, using reliable, economy, environmental protection as the three norms of weighing demand response effect, and guarantee comparability and the cumulative property between index by unified computing method, finally with Field Using Fuzzy Comprehensive Assessment, solve.

The technical solution adopted in the present invention is as follows:

A demand response effect evaluation method based on Fuzzy Analysis, comprises the following steps:

1) determine demand response dispatching effect evaluation index, described index comprises reliability index, economic index and feature of environmental protection index;

2) determine demand response effect assessment grade and pass judgment on collection;

Demand response effect assessment grade is divided into 6 grades from high to low, is defined as and passes judgment on collection, by described step 1) each index set respectively 6 quantized intervals, 6 quantized intervals of described 6 grades and each index are corresponding one by one;

3) determine demand response effect assessment set of factors;

4) determine the judgment matrix R of single index set of factors _k(k=1,2,3);

5) determine the weight coefficient matrix of factor of evaluation collection;

6) the fuzzy evaluation result of computation requirement response scheduling effect.

Aforesaid step 1), in, described reliability index comprises response mode reliability index R _type, response time reliability index R _timewith response Capacity Reliability index R _cap, wherein:

Response mode reliability index R _typerefer to that the number of users by the scheduling of automatic response mode participation demand response accounts for the ratio of all participating user numbers, computing formula is suc as formula shown in (1):

$R_{\mathrm{type}}=\frac{\underset{i\∈n}{\mathrm{\Σ}}{R}_i\left(\mathrm{con}\right)}{n}---\left(1\right)$

Wherein, n is for participating in the total number of users of demand response scheduling; R _i(con) being i user's control sign, is 0-1 decision variable, when control mode is automatic response mode, and R _i(con)=1, otherwise R _i(con)=0;

Response time reliability index R _timerefer to that actual schedule capacity surpasses the time scale of desirable scheduling capacity 80%;

Response Capacity Reliability index R _caprefer at scheduling capacity and be greater than within the scope of 80% desirable scheduling capacity, the product of weight coefficient and scheduling capacity ratio.

Aforesaid step 1), in, described economic index comprises unit response Capacity Cost E _unitwith the electrical network peak-valley difference E reducing _gap, wherein:

Unit response Capacity Cost E _unitrefer to that under certain event, grid company is the cost that Demand-side unit response capacity is paid, computing formula is as follows,

$E_{\mathrm{unit}}=\frac{\underset{i\∈n}{\mathrm{\Σ}}{p}_i{\∫}_0^t{C}_i\left(t\right)}{\underset{i\∈n}{\mathrm{\Σ}}{\∫}_0^t{C}_i\left(t\right)}---\left(5\right)$

Wherein, n is for participating in the total number of users of demand response scheduling, C _i(t) be the response capacity of i user t period; response total amount for i user in whole dispatching cycle; p _ithe economic compensation giving for electrical network under unit quantity of electricity; response total volume for Demand-side generation;

The electrical network peak-valley difference E reducing _gaprefer within the demand response event phase, because of the ratio that demand response event causes electrical network peak-valley difference to reduce, computing formula is as follows,

$E_{\mathrm{gap}}=(\frac{P_{\mathrm{for}}\left(\max \right)}{P_{\mathrm{for}}\left(\min \right)}-\frac{P\left(\max \right)}{P\left(\min \right)})*100\%---\left(6\right)$

Wherein, P _for(max), P _for(min) electrical network maximum, the minimum load of prediction while not occurring for event, P (max), P (min) is that the electrical network of event in the phase is maximum, minimum load.

Aforesaid step 1), in, described feature of environmental protection index comprises the environmental pollutant discharge B of minimizing _c,Nwith user's energy consumption grade B _level, wherein:

The environmental pollutant discharge B reducing _c,Nrefer to the carbon monoxide, carbon dioxide, nitrogen carbonoxide, sulphuric dioxide, particle and the hydrocarbons discharge capacity that because carrying out demand response event, reduce, computing formula is as follows,

$B_{C,N}={\mathrm{\α}}_j\·\underset{i\∈n}{\mathrm{\Σ}}{\∫}_0^t{C}_i\left(t\right)---\left(7\right)$

Wherein, n is for participating in the total number of users of demand response scheduling, α _j(j=1,2 ..., 6) and be pollutant discharge amount design factor, α ₁for CO emission design factor, α ₂cO2 emissions design factor, α ₃nitrogen carbonoxide Emission amount calculation coefficient, α ₄sO2 emissions design factor, α ₅discharge quantity of particulate matter design factor, α ₆hydrocarbons Emission amount calculation coefficient, for response total volume;

User's energy consumption grade B _levelwith reference to country, provinces and cities, electrical network, put into effect energy consumption index appraisal standards, be divided into 1 grade of energy consumption, 2 grades of energy consumptions and 3 grades of energy consumptions, energy consumption level raises successively.

Aforesaid step 3), in, described response effectiveness evaluation set of factors is divided into two-layer:

The comprehensive evaluation factor collection on upper strata is: U={u ₁, u ₂, u ₃}={ reliability index, economic index, feature of environmental protection index },

The single index set of factors of bottom is respectively: U ₁={ u ₁₁, u ₁₂, u ₁₃}={ response mode reliability index, response time reliability index, response Capacity Reliability index }, U ₂={ u ₂₁, u ₂₂}={ unit response Capacity Cost, the electrical network peak-valley difference of minimizing }, U ₃={ u ₃₁, u ₃₂the environmental pollutant discharge of }={ minimizing, user's energy consumption grade }.

Aforesaid step 4), in, judgment matrix is respectively:

The judgment matrix R of reliability index ₁for: $R_1=\left|\begin{array}{cccccc}{r}_{111}& r_{112}& r_{113}& r_{114}& r_{115}& r_{116}\\ r_{121}& r_{122}& r_{123}& r_{124}& r_{125}& r_{126}\\ r_{131}& r_{132}& r_{133}& r_{134}& r_{135}& r_{136}\end{array}\right|$

The judgment matrix R of economic index ₂for: $R_2=\left|\begin{array}{cccccc}{r}_{211}& r_{212}& r_{213}& r_{214}& r_{215}& r_{216}\\ r_{221}& r_{222}& r_{223}& r_{224}& r_{225}& r_{226}\end{array}\right|$

The judgment matrix R of feature of environmental protection index ₃for: $R_3=\left|\begin{array}{cccccc}{r}_{311}& r_{312}& r_{313}& r_{314}& r_{315}& r_{316}\\ r_{321}& r_{322}& r_{323}& r_{324}& r_{325}& r_{326}\end{array}\right|$

Wherein, certain single factor of line display single index set of factors, the share that the opinion rating of this monofactorial evaluation collection is shared is shown in list.

Aforesaid step 5), in, the weight coefficient matrix of factor of evaluation collection is respectively:

The heavy matrix of coefficients of the described comprehensive evaluation factor centralization of state power is: A=|a ₁a ₂a ₃|, and a ₁+ a ₂+ a ₃=1;

Described single index set of factors weight coefficient matrix is: A ₁=| a ₁₁a ₁₂a ₁₃|, and a ₁₁+ a ₁₂+ a ₁₃=1, A ₂=| a ₂₁a ₂₂|, and a ₂₁+ a ₂₂=1, A ₃=| a ₃₁a ₃₂|, and a ₃₁+ a ₃₂=1,

Wherein, a ₁, a ₂, a ₃for each factor in comprehensive evaluation factor collection U is passed judgment on the weight coefficient of collection, a relatively ₁₁, a ₁₂, a ₁₃for single index set of factors U ₁in each factor relatively pass judgment on the weight coefficient of collection, a ₂₁, a ₂₂for single index set of factors U ₂in each factor relatively pass judgment on the weight coefficient of collection, a ₃₁, a ₃₂for single index set of factors U ₃in each factor relatively pass judgment on the weight coefficient of collection.

Aforesaid step 6), the fuzzy evaluation result of computation requirement response scheduling effect, comprises the following steps:

6-1) with compound operation, obtain single index set of factors U _kthe comprehensive evaluation result B of (k=1,2,3) _k:

B _k＝A _k*R _k＝[b _k1b _k2…b _k6]；

6-2) first solve the judgment matrix R of comprehensive evaluation factor collection U: $R=\left[\begin{array}{c}{B}_1\\ B_2\\ B_3\end{array}\right]=\left[\begin{array}{c}{A}_1*R_1\\ A_2*R_2\\ A_3*R_3\end{array}\right]$

Then obtain the final comprehensive evaluation result B:B=A*R=[b of comprehensive evaluation factor collection U ₁b ₂b ₆]

Wherein, b ₁, b ₂, b ₃, b ₄, b ₅and b ₆represent 6 shares that evaluation rank is shared;

6-3) choose b ₁, b ₂, b ₃, b ₄, b ₅and b ₆middle greatest measure is as this demand response dispatching effect evaluation result.

Compared with prior art, tool of the present invention has the following advantages:

The present invention considers the multiclass factors such as safety, reliable, economy, environmental protection, concrete index account form has been proposed, in relative value mode, guarantee comparability and the cumulative property of index, desired data convenient sources, easy to understand, and solve by Field Using Fuzzy Comprehensive Assessment, can take into account different electrical network targets, there is application wide, the feature that applicability is strong.

Accompanying drawing explanation

Fig. 1 is evaluation index block diagram of the present invention;

Fig. 2 is certain demand response event effect schematic diagram in embodiments of the invention;

Fig. 3 is demand response event effect schematic diagram repeatedly in embodiments of the invention;

Fig. 4 responds Capacity Reliability to calculate schematic diagram in embodiments of the invention;

Fig. 5 is the process flow diagram of appraisal procedure of the present invention.

Embodiment

Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail.

As shown in Figure 5, the demand response effect evaluation method based on Fuzzy Analysis of the present invention, comprises the following steps:

1, determine demand response dispatching effect evaluation index, base oneself upon electrical network demand, gap between evaluation requirement response events and the re-set target of this event, and the actual of this event tells on, determine that demand response dispatching effect evaluation index comprises reliability index, economic index and feature of environmental protection index three major types index altogether, as shown in Figure 1, wherein

1.1, reliability index comprises response time reliability index R _time, response mode reliability index R _typewith response Capacity Reliability index R _cap, particularly,

(1) response mode reliability index

Response mode reliability index R _typerefer to that the number of users by the scheduling of automatic response mode participation demand response accounts for the ratio of all participating user numbers, computing formula is suc as formula shown in (1):

$R_{\mathrm{type}}=\frac{\underset{i\∈n}{\mathrm{\Σ}}{R}_i\left(\mathrm{con}\right)}{n}---\left(1\right)$

Wherein, n is for participating in the total number of users of demand response scheduling; R _i(con) being i user's control sign, is 0-1 decision variable, when control mode is automatic response mode, and R _i(con)=1, R under other control mode _i(con)=0.

(2) response time reliability index

Response time reliability index R _timerefer to that actual schedule capacity surpasses the time scale of desirable scheduling capacity 80%;

Consider that Demand-side resource controllability is poor, the less practical factor that waits of regulation and control gear, demand response ideal curve is generally piecewise function, and every section of function is constant.Fig. 2 is certain demand response event design sketch, carries out altogether demand response scheduling events twice.

In Fig. 2, Δ ₁represent the 1. desirable scheduling capacity of time demand response scheduling events, Δ ₂represent the 2. desirable scheduling capacity of time demand response scheduling events, straight line represents desirable dispatch curve, and dotted line represents 80% desirable dispatch curve, and curve represents actual schedule curve.The 1. in time event, demand response scheduling time is t ₁～t ₅if, assert that scheduling capacity surpasses 80% of desirable scheduling capacity and dispatches successfully, the actual demand response scheduling time is t so ₂～t ₃and t ₄～t ₅.The, 2. in time event, the actual demand response scheduling time is t ₆～t ₇.So, in Fig. 2, the 1. response time reliability index R of time event _{time, 1}with the 2. response time reliability index R of time event _{time, 2}be respectively:

$R_{\mathrm{time},1}=\frac{(t_3-t_2)+(t_5-t_4)}{(t_5-t_1)}*100\%---\left(2\right)$

$R_{\mathrm{time},2}=\frac{(t_7-t_6)}{(t_7-t_6)}*100\%---\left(3\right).$

In Fig. 3, curve 1 is almost a straight line, the most approaching with ideal curve, and response time reliability index is 1; Curve 2 has certain fluctuation, but minimum scheduling capacity is still greater than 80% desirable scheduling capacity, and response time reliability index is still 1; Larger, maximum, the minimum scheduling capacity difference ratio of curve 3 fluctuation reaches 50%, and scheduling capacity is extremely unstable, and now reliability index is less than 1 the response time.For electrical network, for promoting the accuracy of response results, avoid unnecessary economic compensation or impact, response curve preferred sequence is: curve 1 > curve 2 > curves 3.

(3) response Capacity Reliability index

Response Capacity Reliability index R _caprefer at scheduling capacity and be greater than within the scope of 80% desirable scheduling capacity, the product of weight coefficient and scheduling capacity ratio,

In Fig. 4, a, b, c, d, e, f, g, h all represents time period, Δ ₁represent desirable scheduling capacity, dotted line represents 80% desirable dispatch curve, and the dotted line of two comparatively denses represents 90% and 110% desirable dispatch curve.Suppose: when scheduling capacity and desirable scheduling capacity differ ratio in 10%, weight coefficient is 1; When scheduling capacity and desirable scheduling capacity differ ratio 10%-20% or scheduling capacity be greater than desirable scheduling capacity 20% time, weight coefficient is 0.8; In other situation, weight coefficient is 0.In Fig. 4, response Capacity Reliability index is so:

$R_{\mathrm{cap}}=\frac{(c+g)*{\mathrm{\α}}_1+(b+d+f)*{\mathrm{\α}}_2+(a+e)*{\mathrm{\α}}_3}{h}$

$=\frac{(c+g)*1+(b+d+f)*0.8+(a+e)*0}{h}$

$=\frac{(c+g)+(b+d+f)*0.8}{h}*100\%---\left(4\right).$

1.2, economic index comprises unit response Capacity Cost E _unitwith the electrical network peak-valley difference E reducing _gap, wherein:

(1) unit response Capacity Cost E _unitrefer to that under certain event, grid company is the cost that Demand-side unit response capacity is paid, computing formula is suc as formula shown in (5),

$E_{\mathrm{unit}}=\frac{\underset{i\∈n}{\mathrm{\Σ}}{p}_i{\∫}_0^t{C}_i\left(t\right)}{\underset{i\∈n}{\mathrm{\Σ}}{\∫}_0^t{C}_i\left(t\right)}---\left(5\right)$

Wherein, n is for participating in the total number of users of demand response scheduling; C _i(t) be the response capacity of i user t period; response total amount for i user in whole dispatching cycle; p _ithe economic compensation giving for electrical network under unit quantity of electricity; response total volume for Demand-side generation.

(2) the electrical network peak-valley difference E reducing _gaprefer within the demand response event phase, because of the ratio that demand response event causes electrical network peak-valley difference to reduce, computing formula is suc as formula shown in (6),

$E_{\mathrm{gap}}=(\frac{P_{\mathrm{for}}\left(\max \right)}{P_{\mathrm{for}}\left(\min \right)}-\frac{P\left(\max \right)}{P\left(\min \right)})*100\%---\left(6\right)$

Wherein, P _for(max), P _for(min) electrical network maximum, the minimum load of prediction while not occurring for event, P (max), P (min) is that the electrical network of event in the phase is maximum, minimum load.

1.3, feature of environmental protection index comprises the environmental pollutant discharge B of minimizing _c,Nwith user's energy consumption grade B _level, wherein:

(1) the environmental pollutant discharge B reducing _c,Nrefer to the carbon monoxide, carbon dioxide, nitrogen carbonoxide, sulphuric dioxide, particle and the hydrocarbons discharge capacity that because carrying out demand response event, reduce, computing formula is suc as formula shown in (7),

$B_{C,N}={\mathrm{\α}}_j\·\underset{i\∈n}{\mathrm{\Σ}}{\∫}_0^t{C}_i\left(t\right)---\left(7\right)$

Wherein, n is for participating in the total number of users of demand response scheduling; α _j(j=1,2 ..., 6) and be pollutant discharge amount design factor, α ₁for CO emission design factor, α ₂cO2 emissions design factor, α ₃nitrogen carbonoxide Emission amount calculation coefficient, α ₄sO2 emissions design factor, α ₅discharge quantity of particulate matter design factor, α ₆hydrocarbons Emission amount calculation coefficient, for response total volume.

(2) user's energy consumption grade B _levelgenerally with reference to country, provinces and cities, electrical network etc., put into effect energy consumption index appraisal standards, be divided into 1 grade of energy consumption, 2 grades of energy consumptions and 3 grades of energy consumptions, energy consumption level raises successively.

2, determine demand response effect assessment grade and pass judgment on collection

Demand response effect assessment grade is divided into 6 grades from high to low, is defined as and passes judgment on collection, the present invention represents with black, red, orange, yellow, blue, green, and the demand response effect of each grade representative is as follows:

1. black: represent demand response poor effect, there is a big difference with electrical network Expected Results;

2. red: represent demand response weak effect, the overwhelming majority response period fails to meet electrical network Expected Results;

3. orange: represent that demand response effect is poor, the partial response period fails to meet electrical network Expected Results;

4. yellow: to represent that demand response effect is general, substantially meet electrical network Expected Results;

5. blue: to represent that demand response is respond well, meet electrical network Expected Results;

6. green: represent that demand response effect is fine, user responds the best, finely meet electrical network Expected Results.

Therefore passing judgment on collection can be expressed as: V={v ₁, v ₂, v ₃, v ₄, v ₅, v ₆}={ is black, red, orange, and Huang, indigo plant, green.

In example of calculation shows, determined 7 indexs of the first step are set respectively to 6 quantized intervals, corresponding one of each quantized interval is passed judgment on collection.As response time reliability index, can think that this index belongs to " indigo plant " efficiency in 90%-95% interval, be " orange " efficiency in 70%-90% interval, and other by that analogy.The efficiency level judgement of all single indexs is all to advise that by data with existing interval and expert assessment and evaluation determines jointly, as concerning " user's energy consumption grade " this index, difference because of electrical network characteristic, in eastern region, more than 40% one-level efficiency can judge that electrical network is " indigo plant " efficiency, and in west area, can only judge that electrical network is " green " efficiency.That is to say, the quantized interval that evaluation rank is corresponding allows expert to set out from different perspectives, according to data with existing suggestion, certain index is belonged to which class efficiency level and make self evaluation, finally the form of expression with degree of membership reflects the preference degree of expert to the whole efficiency of certain electrical network.

3, determine demand response effect assessment set of factors

The demand response Indexes of Evaluation Effect of the first step is divided into 2 layers:

The comprehensive evaluation factor collection on upper strata is: U={u ₁, u ₂, u ₃}={ reliability index, economic index, feature of environmental protection index },

The single index set of factors of bottom is respectively: U ₁={ u ₁₁, u ₁₂, u ₁₃}={ response mode reliability index, response time reliability index, response Capacity Reliability index }, U ₂={ u ₂₁, u ₂₂}={ unit response Capacity Cost, the electrical network peak-valley difference of minimizing }, U ₃={ u ₃₁, u ₃₂the environmental pollutant discharge of }={ minimizing, user's energy consumption grade }.

4, determine the judgment matrix R of single index set of factors _k(k=1,2,3)

The judgment matrix R of reliability index ₁for: $R_1=\left|\begin{array}{cccccc}{r}_{111}& r_{112}& r_{113}& r_{114}& r_{115}& r_{116}\\ r_{121}& r_{122}& r_{123}& r_{124}& r_{125}& r_{126}\\ r_{131}& r_{132}& r_{133}& r_{134}& r_{135}& r_{136}\end{array}\right|$

The judgment matrix R of economic index ₂for: $R_2=\left|\begin{array}{cccccc}{r}_{211}& r_{212}& r_{213}& r_{214}& r_{215}& r_{216}\\ r_{221}& r_{222}& r_{223}& r_{224}& r_{225}& r_{226}\end{array}\right|$

The judgment matrix R of feature of environmental protection index ₃for: $R_3=\left|\begin{array}{cccccc}{r}_{311}& r_{312}& r_{313}& r_{314}& r_{315}& r_{316}\\ r_{321}& r_{322}& r_{323}& r_{324}& r_{325}& r_{326}\end{array}\right|$

Wherein, certain single factor of line display single index set of factors, the share that the opinion rating of this monofactorial evaluation collection is shared is shown in list; As r ₁₁₅represent that expert is to u ₁₁evaluation in, evaluation rank is " indigo plant " portion, with number percent, represents; r ₁₂₃represent that expert is to u ₁₂evaluation in, evaluation rank is " orange " portion, with number percent, represents; r ₁₃₁represent that expert is to u ₁₃evaluation in, evaluation rank is " black " portion, represents, by that analogy with number percent.

5, determine the weight coefficient matrix of factor of evaluation collection

The heavy matrix of coefficients of the comprehensive evaluation factor centralization of state power is: A=|a ₁a ₂a ₃|, and a ₁+ a ₂+ a ₃=1;

Single index set of factors weight coefficient matrix is respectively: A ₁=| a ₁₁a ₁₂a ₁₃|, and a ₁₁+ a ₁₂+ a ₁₃=1, A ₂=| a ₂₁a ₂₂|, and a ₂₁+ a ₂₂=1, A ₃=| a ₃₁a ₃₂|, and a ₃₁+ a ₃₂=1,

Wherein, a ₁, a ₂, a ₃for each factor in comprehensive evaluation factor collection U is passed judgment on the weight coefficient of collection, a relatively ₁₁, a ₁₂, a ₁₃for single index set of factors U ₁in each factor relatively pass judgment on the weight coefficient of collection, a ₂₁, a ₂₂for single index set of factors U ₂in each factor relatively pass judgment on the weight coefficient of collection, a ₃₁, a ₃₂for single index set of factors U ₃in each factor relatively pass judgment on the weight coefficient of collection.

6, the fuzzy evaluation result of computation requirement response scheduling effect,

First with compound operation, obtain single index set of factors U _kthe comprehensive evaluation result B of (k=1,2,3) _k:

B _k＝A _k*R _k＝[b _k1b _k2…b _k6]；

Then solve the judgment matrix R of comprehensive evaluation factor collection U: $R=\left[\begin{array}{c}{B}_1\\ B_2\\ B_3\end{array}\right]=\left[\begin{array}{c}{A}_1*R_1\\ A_2*R_2\\ A_3*R_3\end{array}\right]$

Then obtain the final comprehensive evaluation result B:B=A*R=[b of comprehensive evaluation factor collection U ₁b ₂b ₆]

Wherein, b ₁, b ₂, b ₃, b ₄, b ₅and b ₆be that 6 evaluation ranks are " black ", " red ", " orange ", " Huang ", " indigo plant " and " green " portion, represents with number percent.The general b that selects ₁, b ₂, b ₃, b ₄, b ₅and b ₆middle greatest measure is as this demand response dispatching effect evaluation result.

For example, suppose A=[0.60.30.1], A ₁=[0.20.40.4], A ₂=[0.50.5], A ₃=[0.60.4]

$R_1=\left[\begin{array}{cccccc}0& 0& 0.3& 0.7& 0& 0\\ 0& 0& 0& 0.4& 0.5& 0.1\\ 0& 0& 0& 0.5& 0.5& 0\end{array}\right],$ $R_2=\left[\begin{array}{cccccc}0& 0& 0.4& 0.6& 0& 0\\ 0& 0& 0& 0.7& 0.3& 0\end{array}\right],$

$R_3=\left[\begin{array}{cccccc}0& 0& 0.2& 0.8& 0& 0\\ 0& 0& 0.2& 0.7& 0.1& 0\end{array}\right]$

So,

B ₁＝[000.060.50.40.04]，B ₂＝[000.20.650.150]，B ₃＝[000.20.760.040]，

$R=\left[\begin{array}{cccccc}0& 0& 0.06& 0.5& 0.4& 0.04\\ 0& 0& 0.2& 0.65& 0.15& 0\\ 0& 0& 0.2& 0.76& 0.04& 0\end{array}\right]$

B＝A*R＝[000.1160.5710.2890.024]

In matrix B, b _max=0.571, through expert's comprehensive evaluation, think that this demand response dispatching effect evaluation result belongs to " Huang " look, i.e. " demand response effect is general, substantially meets electrical network Expected Results ".

Concerning electrical network, the assessment result that the identical index in different regions causes may be not identical, and this need take into full account the difference characteristics such as area, user.And Field Using Fuzzy Comprehensive Assessment is converted into quantitative evaluation according to the degree of membership theory of fuzzy mathematics qualitative evaluation, by fuzzy mathematics, to being subject to things or the object of many factors restriction, make an overall evaluation, there is result clear, the feature that systematicness is strong, can solve preferably problem fuzzy, that be difficult to quantification, be applicable to demand response different stages of development.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although the present invention is had been described in detail with reference to above-described embodiment, those of ordinary skill in the field are to be understood that: still can modify or be equal to replacement the specific embodiment of the present invention, and do not depart from any modification of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of claim scope of the present invention.

Claims (8)

1. the demand response effect evaluation method based on Fuzzy Analysis, is characterized in that: comprise the following steps:

1) determine demand response dispatching effect evaluation index, described index comprises reliability index, economic index and feature of environmental protection index;

2) determine demand response effect assessment grade and pass judgment on collection;

Demand response effect assessment grade is divided into 6 grades from high to low, is defined as and passes judgment on collection, by described step 1) each index set respectively 6 quantized intervals, 6 quantized intervals of described 6 grades and each index are corresponding one by one;

3) determine demand response effect assessment set of factors;

4) determine the judgment matrix R of single index set of factors _k(k=1,2,3);

5) determine the weight coefficient matrix of factor of evaluation collection;

6) the fuzzy evaluation result of computation requirement response scheduling effect.

2. a kind of demand response effect evaluation method based on Fuzzy Analysis according to claim 1, is characterized in that: described step 1), described reliability index comprises response mode reliability index R _type, response time reliability index R _timewith response Capacity Reliability index R _cap, wherein:

Response mode reliability index R _typerefer to that the number of users by the scheduling of automatic response mode participation demand response accounts for the ratio of all participating user numbers, computing formula is as follows:

$R_{\mathrm{type}}=\frac{\underset{i\∈n}{\mathrm{\Σ}}{R}_i\left(\mathrm{con}\right)}{n}---\left(1\right)$

Wherein, n is for participating in the total number of users of demand response scheduling; R _i(con) being i user's control sign, is 0-1 decision variable, when control mode is automatic response mode, and R _i(con)=1, otherwise R _i(con)=0;

Response time reliability index R _timerefer to that actual schedule capacity surpasses the time scale of desirable scheduling capacity 80%;

Response Capacity Reliability index R _caprefer at scheduling capacity and be greater than within the scope of 80% desirable scheduling capacity, the product of weight coefficient and scheduling capacity ratio.

3. a kind of demand response effect evaluation method based on Fuzzy Analysis according to claim 1, is characterized in that: described step 1), described economic index comprises unit response Capacity Cost E _unitwith the electrical network peak-valley difference E reducing _gap, wherein:

Unit response Capacity Cost E _unitrefer to that under certain event, grid company is the cost that Demand-side unit response capacity is paid, computing formula is as follows,

$E_{\mathrm{unit}}=\frac{\underset{i\∈n}{\mathrm{\Σ}}{p}_i{\∫}_0^t{C}_i\left(t\right)}{\underset{i\∈n}{\mathrm{\Σ}}{\∫}_0^t{C}_i\left(t\right)}---\left(5\right)$

Wherein, n is for participating in the total number of users of demand response scheduling, C _i(t) be the response capacity of i user t period; response total amount for i user in whole dispatching cycle; p _ithe economic compensation giving for electrical network under unit quantity of electricity; response total volume for Demand-side generation;

The electrical network peak-valley difference E reducing _gaprefer within the demand response event phase, because of the ratio that demand response event causes electrical network peak-valley difference to reduce, computing formula is as follows,

$E_{\mathrm{gap}}=(\frac{P_{\mathrm{for}}\left(\max \right)}{P_{\mathrm{for}}\left(\min \right)}-\frac{P\left(\max \right)}{P\left(\min \right)})*100\%---\left(6\right)$

Wherein, P _for(max), P _for(min) electrical network maximum, the minimum load of prediction while not occurring for event, P (max), P (min) is that the electrical network of event in the phase is maximum, minimum load.

4. a kind of demand response effect evaluation method based on Fuzzy Analysis according to claim 1, is characterized in that: described step 1), described feature of environmental protection index comprises the environmental pollutant discharge B of minimizing _c,Nwith user's energy consumption grade B _level, wherein:

The environmental pollutant discharge B reducing _c,Nrefer to the carbon monoxide, carbon dioxide, nitrogen carbonoxide, sulphuric dioxide, particle and the hydrocarbons discharge capacity that because carrying out demand response event, reduce, computing formula is as follows

$B_{C,N}={\mathrm{\α}}_j\·\underset{i\∈n}{\mathrm{\Σ}}{\∫}_0^t{C}_i\left(t\right)---\left(7\right)$

Wherein, n is for participating in the total number of users of demand response scheduling, α _j(j=1,2 ..., 6) and be pollutant discharge amount design factor, α ₁for CO emission design factor, α ₂cO2 emissions design factor, α ₃nitrogen carbonoxide Emission amount calculation coefficient, α ₄sO2 emissions design factor, α ₅discharge quantity of particulate matter design factor, α ₆hydrocarbons Emission amount calculation coefficient, for response total volume;

User's energy consumption grade B _levelwith reference to country, provinces and cities, electrical network, put into effect energy consumption index appraisal standards, be divided into 1 grade of energy consumption, 2 grades of energy consumptions and 3 grades of energy consumptions, energy consumption level raises successively.

5. a kind of demand response effect evaluation method based on Fuzzy Analysis according to claim 1, is characterized in that: described step 3), described response effectiveness evaluation set of factors is divided into two-layer:

The comprehensive evaluation factor collection on upper strata is: U={u ₁, u ₂, u ₃}={ reliability index, economic index, feature of environmental protection index },

The single index set of factors of bottom is respectively: U ₁={ u ₁₁, u ₁₂, u ₁₃}={ response mode reliability index, response time reliability index, response Capacity Reliability index }, U ₂={ u ₂₁, u ₂₂}={ unit response Capacity Cost, the electrical network peak-valley difference of minimizing }, U ₃={ u ₃₁, u ₃₂the environmental pollutant discharge of }={ minimizing, user's energy consumption grade }.

6. a kind of demand response effect evaluation method based on Fuzzy Analysis according to claim 1, is characterized in that: described step 4), judgment matrix is respectively:

The judgment matrix R of reliability index ₁for: $R_1=\left|\begin{array}{cccccc}{r}_{111}& r_{112}& r_{113}& r_{114}& r_{115}& r_{116}\\ r_{121}& r_{122}& r_{123}& r_{124}& r_{125}& r_{126}\\ r_{131}& r_{132}& r_{133}& r_{134}& r_{135}& r_{136}\end{array}\right|$

The judgment matrix R of economic index ₂for: $R_2=\left|\begin{array}{cccccc}{r}_{211}& r_{212}& r_{213}& r_{214}& r_{215}& r_{216}\\ r_{221}& r_{222}& r_{223}& r_{224}& r_{225}& r_{226}\end{array}\right|$

The judgment matrix R of feature of environmental protection index ₃for: $R_3=\left|\begin{array}{cccccc}{r}_{311}& r_{312}& r_{313}& r_{314}& r_{315}& r_{316}\\ r_{321}& r_{322}& r_{323}& r_{324}& r_{325}& r_{326}\end{array}\right|$

Wherein, certain single factor of line display single index set of factors, the share that the opinion rating of this monofactorial evaluation collection is shared is shown in list.

7. a kind of demand response effect evaluation method based on Fuzzy Analysis according to claim 1, is characterized in that: described step 5), the weight coefficient matrix of factor of evaluation collection is respectively:

The heavy matrix of coefficients of the described comprehensive evaluation factor centralization of state power is: A=|a ₁a ₂a ₃|, and a ₁+ a ₂+ a ₃=1;

Described single index set of factors weight coefficient matrix is: A ₁=| a ₁₁a ₁₂a ₁₃|, and a ₁₁+ a ₁₂+ a ₁₃=1, A ₂=| a ₂₁a ₂₂|, and a ₂₁+ a ₂₂=1, A ₃=| a ₃₁a ₃₂|, and a ₃₁+ a ₃₂=1,

Wherein, a ₁, a ₂, a ₃for each factor in comprehensive evaluation factor collection U is passed judgment on the weight coefficient of collection, a relatively ₁₁, a ₁₂, a ₁₃for single index set of factors U ₁in each factor relatively pass judgment on the weight coefficient of collection, a ₂₁, a ₂₂for single index set of factors U ₂in each factor relatively pass judgment on the weight coefficient of collection, a ₃₁, a ₃₂for single index set of factors U ₃in each factor relatively pass judgment on the weight coefficient of collection.

8. a kind of demand response effect evaluation method based on Fuzzy Analysis according to claim 1, is characterized in that: described step 6), the fuzzy evaluation result of computation requirement response scheduling effect, comprises the following steps:

6-1) with compound operation, obtain single index set of factors U _kthe comprehensive evaluation result B of (k=1,2,3) _k:

B _k＝A _k*R _k＝[b _k1b _k2…b _k6]；

6-2) first solve the judgment matrix R of comprehensive evaluation factor collection U: $R=\left[\begin{array}{c}{B}_1\\ B_2\\ B_3\end{array}\right]=\left[\begin{array}{c}{A}_1*R_1\\ A_2*R_2\\ A_3*R_3\end{array}\right]$

Then obtain the final comprehensive evaluation result B:B=A*R=[b of comprehensive evaluation factor collection U ₁b ₂b ₆]

Wherein, b ₁, b ₂, b ₃, b ₄, b ₅and b ₆represent 6 shares that evaluation rank is shared;

6-3) choose b ₁, b ₂, b ₃, b ₄, b ₅and b ₆middle greatest measure is as this demand response dispatching effect evaluation result.