CN114169790B - User ordered electricity utilization potential comprehensive evaluation method considering relevance among indexes - Google Patents
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Abstract
The invention relates to a user ordered electricity utilization potential comprehensive evaluation method considering the relevance among indexes, which comprises the following steps: step S1, acquiring the hour-level electricity consumption data of a user within one month and the hour-level load data of a power grid within a local province, and calculating an electricity utilization regulation potential index; step S2, calculating objective weights of characteristic indexes by adopting an optimization algorithm considering the relativity among indexes according to the obtained power utilization regulation potential indexes; and step S3, estimating the maximum reducible load based on the objective weight of the characteristic index, and further obtaining the ordered electricity utilization potential score. The invention realizes comprehensive evaluation of the ordered electricity utilization potential of the user, estimates the electric energy which can be saved by the ordered electricity utilization potential of the user according to the related potential characteristic indexes, and further improves the rationality of electricity utilization regulation.
Description
Technical Field
The invention relates to a user ordered electricity utilization potential comprehensive evaluation method considering the relevance among indexes.
Background
With the sustainable development of the economy in China, the increase of the power demand is increasingly remarkable, but the problem of unbalanced risk still exists in the power supply and demand in the peak period. In order to solve the problem of shortage of power supply, the implementation of orderly power utilization is still an important load management means in China recently. In order to improve the scientificity and rationality of the ordered electricity utilization plans, reduce the participation of manpower, prevent the human intervention, improve the intelligent level of the planning, and ensure that the total misplaced load among each group of the ordered electricity utilization plans is as uniform as possible.
Because the power utilization modes of different enterprises are different, the response sensitivity to power grid dispatching is also different, so that the existing ordered power utilization plan is adopted to carry out differentiated dispatching on users of different enterprises at power utilization peaks, and the expected result is often different from the dispatching effect in the follow-up actual operation.
Disclosure of Invention
Therefore, the invention aims to provide a comprehensive evaluation method for the ordered electricity utilization potential of the user by considering the relevance among indexes, which realizes comprehensive evaluation for the ordered electricity utilization potential of the user, evaluates the electric energy which can be saved by the ordered electricity utilization potential of the user according to the related potential characteristic indexes, and further improves the rationality of electricity utilization regulation.
In order to achieve the above purpose, the invention adopts the following technical scheme:
A user ordered electricity utilization potential comprehensive evaluation method considering the relevance among indexes comprises the following steps:
Step S1, acquiring the hour-level electricity consumption data of a user within one month and the hour-level load data of a power grid within a local province, and calculating an electricity utilization regulation potential index;
step S2, calculating objective weights of characteristic indexes by adopting an optimization algorithm considering the relativity among indexes according to the obtained power utilization regulation potential indexes;
and step S3, estimating the maximum reducible load based on the objective weight of the characteristic index, and further obtaining the ordered electricity utilization potential score.
Further, the step S1 specifically includes:
Step S11, acquiring the hour-level electricity consumption data of a user within one month and the hour-level load data of a power grid within a local province;
Step S12, analyzing according to the power consumption data of enterprise users and the power grid load data to respectively obtain a peak avoidance characteristic index Z 1 and a peak staggering characteristic index Z 2 which are evaluated in a daily scale, a circle-rest characteristic index Z 3 which is evaluated in a weekly scale and a maintenance characteristic index Z 4 which is evaluated in a monthly scale;
Wherein the peak avoidance characteristic index Z 1 consists of two sub-indexes of a power consumption wave power rate Z 11 and a power consumption peak-valley difference Z 12; the peak staggering characteristic index Z 2 consists of two sub-indexes of peak flat power utilization ratio Z 21 and peak staggering load difference Z 22; the rotation characteristic index Z 3 consists of two sub-indexes of Zhou Xiu load difference Z 31 and weekly load drop rate Z 32; the overhaul characteristic index Z 4 consists of two sub-indexes of an overhaul load difference Z 41 and an overhaul rate Z 42;
And S13, carrying out dimensionless normalization processing on all index sets Z= { Z 1,Z2,Z3,Z4}＝{Z11,Z12,...,Z42 }.
Further, the dimensionless normalization processing adopts a Z-score normalization algorithm, specifically:
Let the potential index set of the mu-th user be N is the number of users all participating in the potential assessment
① Calculating the average value of each potential index:
② Calculating the variance of each potential index:
③ Dimensionless normalization of potential indexes by means of average value and standard deviation of potential indexes
Further, in the step S2, an optimization algorithm considering the relevance between indexes is adopted to objectively assign weight to Z *＝{Z1 *,Z2 *,Z3 *,Z4 *, specifically:
Step S21, identification coefficient vector alpha= [ alpha 1,α2,α3,α4 ]
Wherein ρ i is the ith index dataStandard deviation of (2);
Step S22, conflict coefficient vector v= [ v 1,v2,v3,v4 ]
The conflict coefficient is obtained from the data information in the standardized coefficient matrix and the correlation coefficient matrix
Wherein r' ij is the pearson correlation coefficient between the ith and jth indices; t ij is the size of the conflict between the ith index and the jth index;
step S23, re-ordering each component in the conflict vector v from small to large to obtain an ordered vector lambda, and calculating the difference coefficient g and the undetermined parameter beta of each component in the vector lambda
Step S24, calculating objective weight vector w= [ w 1,w2,w3,w4 ]
wi＝βvi+(1-β)ci。
Further, the maximum load reducible estimate is specifically as follows:
The mu-th user peak avoidance, maintenance, rotation and peak staggering load reduction are grouped for calculation, wherein the peak regulation group consists of the peak avoidance load reduction of the user after the peak avoidance instruction is sent to the user, and the stop group consists of the sum of the peak avoidance load reduction of the user after the maintenance, rotation and peak staggering instruction is sent to the user
① Peak regulating group capable of reducing load P 1μ
Epsilon-peak avoidance load reduction coefficient of 0.7 to 0.85
Delta 1 - -Peak avoidance switching coefficient, whenDelta 1 takes 1, whereas takes 0;
Delta 2 - -off-peak switching coefficient, when Delta 2 takes 1, whereas takes 0;
② Load P 2μ can be reduced by stopping and resting group
Delta 3 - -coefficient of switching on and off in rotation, whenDelta 3 takes 1, whereas takes 0;
Delta 4 - -maintenance of the switching coefficient, when Delta 4 takes 1, whereas takes 0;
③ Maximum load reducible P μ
Pμ＝P1μ+P2μ。
Further, the orderly electricity utilization potential scoring calculation specifically comprises the following steps:
Compared with the prior art, the invention has the following beneficial effects:
The invention can obtain the peak avoidance, peak staggering, rotation and maintenance characteristic indexes through analyzing the electricity consumption of the user, further estimate the maximum load reduction after ordered electricity consumption scheduling, obtain the comprehensive score of the ordered electricity consumption potential of the user, be beneficial to the business personnel to quickly compile the ordered electricity consumption plan, and be more beneficial to ensuring the safe and stable operation of the power grid and the reliable supply of the electric power.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and examples.
Referring to fig. 1, the invention provides a method for comprehensively evaluating the orderly electric potential of a user by considering the relevance among indexes, which comprises the following steps:
Step S1, acquiring the hour-level electricity consumption data of a user within one month and the hour-level load data of a power grid within a local province, and calculating an electricity utilization regulation potential index;
step S2, calculating objective weights of characteristic indexes by adopting an optimization algorithm considering the relativity among indexes according to the obtained power utilization regulation potential indexes;
and step S3, estimating the maximum reducible load based on the objective weight of the characteristic index, and further obtaining the ordered electricity utilization potential score.
In this embodiment, the power consumption regulation potential index is calculated as follows:
Extracting the hour-level electricity consumption data of an enterprise user within one month and the hour-level load data of a power grid within a province of the affiliated area; and analyzing according to the power consumption data of the enterprise user and the power grid load data to respectively obtain a peak avoidance characteristic index Z 1 and a peak staggering characteristic index Z 2 which are evaluated in a daily scale, a rotation characteristic index Z 3 which is evaluated in a monthly scale and a maintenance characteristic index Z 4 which is evaluated in the monthly scale.
The peak avoidance characteristic index Z 1 consists of two sub-indexes of the electricity consumption wave power Z 11 and the electricity consumption peak-valley difference Z 12. The peak staggering characteristic index Z 2 consists of two sub-indexes of peak flat power utilization ratio Z 21 and peak staggering load difference Z 22. The rotation characteristic index Z 3 consists of two sub-indexes of Zhou Xiu load difference Z 31 and weekly load drop rate Z 32. The overhaul characteristic index Z 4 consists of two sub-indexes of an overhaul load difference Z 41 and an overhaul rate Z 42.
And carrying out dimensionless standardization treatment on all index sets Z= { Z 1,Z2,Z3,Z4}＝{Z11,Z12,...,Z42 }, and selecting a Z-score standardization algorithm. Let the potential index set of the mu-th user beN is the number of users who all participate in the potential assessment. The standardized calculation method is as follows:
① Calculating the average value of each potential index:
② Calculating the variance of each potential index:
③ And carrying out dimensionless standardization on each potential index by using the mean value and standard deviation of each potential index.
For four standardized characteristic indexesThe internal sub-indexes are weighted one by one, the weight of the sub-index in each characteristic index is reset to 0.5, and the calculation method is as follows:
In this embodiment, an optimization algorithm considering the relevance between indexes is adopted to objectively assign weight to Z *＝{Z1 *,Z2 *,Z3 *,Z4 *, specifically:
⑴ Discrimination coefficient vector α= [ α 1,α2,α3,α4 ]
Wherein ρ i is the ith index dataStandard deviation of (2)
⑵ Conflict coefficient vector v= [ v 1,v2,v3,v4 ]
The conflict coefficient is obtained by normalizing the data information in the coefficient matrix and solving the correlation coefficient matrix.
Wherein r' ij is the pearson correlation coefficient between the ith and jth indices; t ij is the size of the conflict between the ith index and the jth index
⑶ Calculating the undetermined coefficient beta
And (3) reordering all components in the conflict vector v from small to large to obtain an ordered vector lambda, and calculating a difference coefficient g and a to-be-determined parameter beta of all components in the vector lambda.
⑷ Calculate the objective weight vector w= [ w 1,w2,w3,w4 ]
wi＝βvi+(1-β)ci。
In this embodiment, the comprehensive evaluation is made up of two parts, namely a maximum cut-down load evaluation and an ordered potential score.
⑴ Maximum cut load estimation
The method comprises the steps of grouping and calculating peak avoidance, maintenance, rotation and peak staggering load reduction of a mu user, wherein the peak regulation group is formed by independently carrying out peak avoidance load reduction on the user after sending out peak avoidance instructions to the user, and the stop group is formed by carrying out sum of load reduction on the user after sending out maintenance, rotation and peak staggering instructions to the user.
① Peak regulating group capable of reducing load P 1μ
Epsilon-peak avoidance load reduction coefficient of 0.7 to 0.85
Delta 1 - -Peak avoidance switching coefficient, whenDelta 1 takes 1 and vice versa takes 0.
Delta 2 - -off-peak switching coefficient, whenDelta 2 takes 1 and vice versa takes 0.
② Load P 2μ can be reduced by stopping and resting group
Delta 3 - -coefficient of switching on and off in rotation, whenDelta 3 takes 1 and vice versa takes 0.
Delta 4 - -maintenance of the switching coefficient, whenDelta 4 takes 1 and vice versa takes 0.
③ Maximum load reducible P μ
Pμ＝P1μ+P2μ
⑵ Ordered electropotential scoring
Composite score S
Users rated "good" for orderly power use potential should be prioritized for participation in orderly power use, users rated "good" next to users rated "bad" are generally not scheduled for participation in orderly power use regulation.
The foregoing description is only of the preferred embodiments of the invention, and all changes and modifications that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (4)
1. The comprehensive evaluation method for the ordered electricity utilization potential of the user taking the relevance among indexes into consideration is characterized by comprising the following steps of:
Step S1, acquiring the hour-level electricity consumption data of a user within one month and the hour-level load data of a power grid within a local province, and calculating an electricity utilization regulation potential index;
step S2, calculating objective weights of characteristic indexes by adopting an optimization algorithm considering the relativity among indexes according to the obtained power utilization regulation potential indexes;
Step S3, estimating the maximum reducible load based on the objective weight of the characteristic index, and further obtaining the ordered electricity utilization potential score;
Step S2 adopts an optimization algorithm considering the relevance among indexes to objectively weight Z *＝{Z1 *,Z2 *,Z3 *,Z4 *, and specifically comprises the following steps:
Step S21, identification coefficient vector alpha= [ alpha 1,α2,α3,α4 ]
Wherein ρ i is the ith index dataStandard deviation of (2);
Step S22, conflict coefficient vector v= [ v 1,v2,v3,v4 ]
The conflict coefficient is obtained from the data information in the standardized coefficient matrix and the correlation coefficient matrix
Wherein r' ij is the pearson correlation coefficient between the ith and jth indices; t ij is the size of the conflict between the ith index and the jth index;
step S23, re-ordering each component in the conflict vector v from small to large to obtain an ordered vector lambda, and calculating the difference coefficient g and the undetermined parameter beta of each component in the vector lambda
Step S24, calculating objective weight vector w= [ w 1,w2,w3,w4 ]
wi＝βvi+(1-β)ci；
The maximum load reducible estimate is specifically as follows:
The mu-th user peak avoidance, maintenance, rotation and peak staggering load reduction are grouped for calculation, wherein the peak regulation group consists of the peak avoidance load reduction of the user after the peak avoidance instruction is sent to the user, and the stop group consists of the sum of the peak avoidance load reduction of the user after the maintenance, rotation and peak staggering instruction is sent to the user
① Peak regulating group capable of reducing load P 1μ
Epsilon-peak avoidance load reduction coefficient of 0.7 to 0.85
Delta 1 - -Peak avoidance switching coefficient, whenDelta 1 takes 1, whereas takes 0;
Delta 2 - -off-peak switching coefficient, when Delta 2 takes 1, whereas takes 0;
② Load P 2μ can be reduced by stopping and resting group
Delta 3 - -coefficient of switching on and off in rotation, whenDelta 3 takes 1, whereas takes 0;
Delta 4 - -maintenance of the switching coefficient, when Delta 4 takes 1, whereas takes 0;
③ Maximum load reducible P μ
Pμ＝P1μ+P2μ。
2. The method for comprehensively evaluating the orderly user power utilization potential by considering the relevance among indexes according to claim 1, wherein the step S1 is specifically:
Step S11, acquiring the hour-level electricity consumption data of a user within one month and the hour-level load data of a power grid within a local province;
Step S12, analyzing according to the power consumption data of enterprise users and the power grid load data to respectively obtain a peak avoidance characteristic index Z 1 and a peak staggering characteristic index Z 2 which are evaluated in a daily scale, a circle-rest characteristic index Z 3 which is evaluated in a weekly scale and a maintenance characteristic index Z 4 which is evaluated in a monthly scale;
Wherein the peak avoidance characteristic index Z 1 consists of two sub-indexes of a power consumption wave power rate Z 11 and a power consumption peak-valley difference Z 12; the peak staggering characteristic index Z 2 consists of two sub-indexes of peak flat power utilization ratio Z 21 and peak staggering load difference Z 22; the rotation characteristic index Z 3 consists of two sub-indexes of Zhou Xiu load difference Z 31 and weekly load drop rate Z 32; the overhaul characteristic index Z 4 consists of two sub-indexes of an overhaul load difference Z 41 and an overhaul rate Z 42;
And S13, carrying out dimensionless normalization processing on all index sets Z= { Z 1,Z2,Z3,Z4}＝{Z11,Z12,...,Z42 }.
3. The method for comprehensively evaluating the orderly user power utilization potential by considering the relevance among indexes according to claim 2, wherein the dimensionless normalization process adopts a Z-score normalization algorithm, and is specifically as follows:
Let the potential index set of the mu-th user be N is the number of users all participating in the potential assessment
① Calculating the average value of each potential index:
② Calculating the variance of each potential index:
③ Dimensionless normalization of potential indexes by means of average value and standard deviation of potential indexes
4. The method for comprehensively evaluating the ordered electric potential of the user considering the relatedness among indexes according to claim 1, wherein the calculation of the ordered electric potential score is specifically as follows:
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