CN115953084A - Quantitative verification test method for utility of demand response flexible resource characteristics - Google Patents
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Abstract
The application relates to a method for quantitatively verifying and testing the utility of a demand response flexible resource characteristic, which comprises the following specific steps: s1, establishing a load potential quantification model, extracting power utilization common characteristics of users, and forming a quantitative evaluation load response potential index detail; s2, selecting loads of large industrial users and commercial building users according to the power utilization common characteristics of the users and the task requirements of demand response, setting a rating characteristic index of each load, and performing quantitative evaluation according to the set indexes to form a test evaluation method; and S3, selecting a demand response resource library according to the evaluation method to carry out a demand response potential test, and outputting an evaluation result. According to the method, a flexible quantification model is provided for the response potential of large industrial users and commercial buildings, the response case is executed by combining the actual condition of the user, the feasibility of the model is guaranteed, the social demand and the benefit of the user are further coordinated and linked by a reference method, more response resources are promoted to be excavated, and the response enthusiasm of the user for participating in demand is improved.
Description
Technical Field
The application relates to the field of power demand side management, in particular to a method for quantitatively verifying and testing utility of demand response flexible resource characteristics.
Background
With the rapid development of economy, a large amount of conventional units are replaced by new energy, and interactive devices such as distributed energy and energy storage are widely connected, so that the double-high characteristic of the power system is increasingly remarkable, and huge challenges are brought to the balance adjustment of a power grid. The electricity demand presents the characteristics of double peaks in winter and summer, the peak-valley difference is continuously enlarged, and the difficulty of power guarantee supply is increased. Demand side response is more emphasized, demand side resources are guided to be matched with a supply side independently, intelligent interaction of power supply and demand is achieved, and system new energy accepting development capacity is enhanced. At present, many problems still exist in demand response, such as the reliability of demand response resources needs to be improved, the power demand response resources are not enough reserved, and the like.
Disclosure of Invention
The method comprises the steps of selecting a demand response evaluation index and a standard to calculate corresponding parameter values, calculating the weight of each parameter by using an entropy weight method after the corresponding parameter values are calculated, evaluating by adopting TOPSIS (technique for order preference by similarity to similarity), and determining the adjustable potential of the flexible response resource according to the score.
In order to achieve the above purpose, the present application provides the following technical solutions:
the embodiment of the application provides a method for verifying and testing utility quantification of flexible resource characteristics in demand response, which comprises the following specific steps:
s1, establishing a load potential quantification model, extracting the electricity utilization common characteristics of users, and forming a quantitative evaluation load response potential index detail;
s2, selecting loads of large industrial users and commercial building users according to the power utilization common characteristics of the users and the task requirements of demand response, setting a rating characteristic index of each load, and performing quantitative evaluation according to the set indexes to form a test evaluation method;
and S3, selecting a demand response resource library according to the evaluation method to carry out a demand response potential test, and outputting an evaluation result.
In the step S1, a load potential quantification model is established by approximating an ideal solution ordering TOPSIS method.
And in the step S2, a demand response potential test evaluation method of the large industrial user load and the commercial building user load is divided according to the user power utilization commonality characteristics and the task needs of demand response.
In the step S2, a rating characteristic index is set for each load, a multi-time scale flexible resource response potential index system is constructed from 3 aspects of time-staggered potential, alternate break potential and peak avoidance potential, an adjustable potential evaluation model of the industrial and commercial enterprises is constructed, the set indexes quantitatively evaluate the three loads respectively.
The evaluation result in the step S3 is realized by the following method:
acquiring input operation data of a designated area, and performing data cleaning on original data;
obtaining feature-fused operation data according to the cleaned data, and calculating user indexes of large-scale industrial users and commercial buildings by using the operation data;
and finally, evaluating by adopting TOPSIS, determining the adjustable potential of the flexible response resource according to the score, and quantitatively testing the characteristic index of the flexible resource and the response potential of the typical demand scene.
Compared with the prior art, the invention has the beneficial effects that: the flexible quantification model is provided for the response potential of large industrial users and commercial buildings, the response case is executed by combining the actual condition of the user, the feasibility of the model is verified, the social requirement and the benefit of the user are further coordinated and linked through a reference method, more response resources are promoted to be excavated, and the response enthusiasm of the user for participating in the requirement is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present application.
Detailed description of the preferred embodiments
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
Referring to fig. 1, an embodiment of the present application provides a method for verifying and testing utility quantification of a flexible resource characteristic in response to a demand, which includes the following steps:
s1, establishing a load potential quantification model, extracting the electricity utilization common characteristics of users, and forming a quantitative evaluation load response potential index detail;
s2, selecting loads of large industrial users and commercial building users according to the power utilization common characteristics of the users and the task requirements of demand response, setting a rating characteristic index of each load, and performing quantitative evaluation according to the set indexes to form a test evaluation method;
and S3, selecting a demand response resource library according to the evaluation method to carry out a demand response potential test, and outputting an evaluation result.
Principle of evaluation matrix
Based on a load response potential index system, supposing that P1 represents time-staggered potential, P2 represents alternate break potential, and P3 represents peak avoidance potential; set of participating users
There are m different users; vector quantity
Indicates potential->
N indices. Each potential can establish an initial potential evaluation moment S as follows: />
Wherein, y ij The value of the jth index for user i.
The evaluation matrix provides basic information of the analysis problem, and the preprocessing of the subsequent load data and the establishment of a quantitative model are used as the analysis basis.
2. Index value preprocessing
(1) Statistical averaging method
Due to the difference of the dimensions of different indexes, the indexes are required to be subjected to standardization treatment before the quantitative model is introduced. In order to avoid serious distortion of the quantization result caused by too large difference of index values of different users, a statistical averaging method similar to a scoring method can be adopted to set a percentile average value M, the average value of potential index values in a user set K is positioned at M, and the transformation is specifically carried out by using the following formula
Wherein
The average value of j index of each user; />
The maximum value of the jth index of each user; m is a constant and takes a value of 50% -75%. Y in the matrix S is determined by statistical averaging ij Is converted into m ij 。
(2) Vector normalization
After statistical averaging, the original index value has been subjected to de-dimensionalization and normalization. The data after statistical averaging is further vector normalized using the following equation.
The change of vector normalization is linear, and the sum of squares of the same index value of the normalized user is 1.
(3) Method for solving characteristic index weight by entropy weight method
The key of the multi-index decision problem lies in solving the contradiction between different indexes, so the concept of weight needs to be introduced. The weight is a measure of the importance of different indexes, and can reflect the degree of difference between the indexes and the reliability of the indexes. In order to ensure the objectivity of the result, the entropy weight method principle is adopted to adaptively solve the potential P i Weights of different indices. The entropy weight method can avoid the interference of human factors to a great extent, overcomes the defect of overlarge subjectivity when determining the index weight, and comprises the following specific steps,
1) The potential evaluation matrix after the pretreatment is normalized according to columns, as shown in formula (4), wherein each column is
。
Wherein i = 1,2, \8230;, m; j = 1,2, \8230;, n.
2) The entropy of the jth index in the potential evaluation matrix is calculated according to the columns as follows:
，/>
wherein j = 1,2, \8230, n;
。
3) Calculating the difference coefficient of the j index in the potential evaluation matrix
Comprises the following steps:
wherein j = 1,2, \8230;, n. The difference coefficient is opposite to the entropy, and the larger the difference coefficient is, the larger the difference between the indexes is, the larger the effect on the decision is, and therefore the corresponding weight is larger.
4) The weight is determined. The weight of the jth index is:
wherein j = 1,2, \8230;, n.
Finally, a weighted evaluation matrix of the potential Pi after pretreatment is obtained
Comprises the following steps:
(4) Quantification of load control potential based on TOPSIS method
The TOPSIS method calculates the relative closeness of the index value of each user and the positive and negative ideal solutions, and then obtains the quality sequence of the evaluation object. The TOPSIS method has the characteristics of easiness in understanding, simplicity and convenience in calculation, reasonable evaluation result, flexibility in application and the like, and is widely applied to the fields of social economy, engineering technology and the like. The distance between the weighted index values of different users in the weighted evaluation matrix S' and the positive and negative ideal solutions is calculated based on the TOPSIS method, and the potentials of all the users in the user set K are ranked.
The specific TOPSIS method for quantifying the load control potential comprises the following steps.
1) Based on a weighted evaluation matrix
Determining a positive ideal solution>
And negative ideal solution>
. Let positive ideal solution>
Is/is>
Ideal negative releasing/selecting>
Is/is>
。
For the benefit type index, i.e. the index with larger numerical value and better numerical value, the following indexes are provided:
for cost-type indexes, i.e., indexes with smaller numerical values and better numerical values, there are:
2) And calculating the distance between each index value of the user and the positive ideal solution and the negative ideal solution.
Distance between index value of user i and positive ideal solution
Comprises the following steps: />
Distance between index value of user i and negative ideal solution
Comprises the following steps:
3) And calculating a comprehensive ranking value Ri of each user potential as shown in the formula (23).
And taking the calculated comprehensive ranking value Ri as a potential comprehensive quantitative value. And finally, arranging the comprehensive quantized values Ri of the corresponding potentials of the users from large to small, and reflecting the order of the advantages and the disadvantages.
Based on the quantization model, 3 potential quantization matrixes can be obtained finally. Set of participating users
By means of the vector>
A comprehensive quantified value representing the user's wrong time potential, in terms of a vector @>
Comprehensive quantitative value for representing the user's alternate break potential, using vector
And representing the comprehensive quantization value of the peak avoidance potential of the user, wherein the potential quantization matrix is as follows:
and finally, screening the large users according to the final quantization result on the basis of quantizing the potential of the large users.
The test evaluation methods for the loads of the large industrial users and the commercial building users in the step S2 are respectively as follows:
1. load demand response test evaluation method for large-scale industrial users and commercial building users
The large-industry user test evaluation method is mainly based on load data of large-industry users and is used for fully extracting power utilization characteristics of the users under different time quantities. And constructing a multi-time scale flexible resource response potential index system from 3 aspects of time staggering potential, alternate break potential and peak avoidance potential, and constructing a large-scale industrial user adjustable potential evaluation model containing operating characteristics and economic characteristics. The common load regulation and control modes comprise peak staggering and peak avoiding, wherein the peak staggering is carried out from the week scale and the daily scale in alternate break and alternate time respectively, and the peak avoiding refers to the reduction of the power consumption requirement of the controllable load in the peak time period. The three time scale potentials of time staggering, peak avoiding and alternate rest are selected, the high-power-consumption industries such as steel, electrolytic aluminum, textile and the like in a certain area are selected for research, and high-quality adjustable load resources of time staggering, peak avoiding and alternate rest are screened out.
According to different time scales, potential evaluation systems of the user in the aspects of time staggering, alternate break and peak avoidance are established, potential values of the user participating in different load responses are evaluated respectively, namely a multi-time scale load response potential index system is established and formed according to load evaluation modes and extracted main characteristics under different time scales, and the potential evaluation systems are shown in table 1.
TABLE 1 Multi-time scale load response potential index system
1.1 Peak avoidance potential index
(1) Peak time average load difference coefficient
If a user has a large peak avoidance potential, the user must have a relatively high load during the peak period of the load of the whole network, so as to promote the formation of the load peak of the whole network. Therefore, a peak time average load difference coefficient is defined, and when the index value is larger than 0, the user is reflected to contribute to the formation of the peak load of the whole network, and the larger the index value is, the larger the contribution is, and the larger the corresponding peak avoidance potential is. The concrete formula is as follows:
wherein the content of the first and second substances,
the average value of the user load in the peak period; />
The average value of the load of the user all day.
(2) Maximum load versus temperature dependence
By calculating the correlation index of the maximum load and the temperature
It is indirectly reflected whether the load spike is caused by a temperature controlled load. The higher the correlation is, the load is mostly the temperature control load, and since the measures for shutting down the temperature control load are convenient and quick and have small economic impact, the user should be prioritized to implement the peak avoidance type load control.
(3) Load avoiding peak
The peak avoiding load refers to the load amount of a user which is rapidly reduced by emergency shutdown equipment in the peak period of power utilization. But does not mean to reduce to 0, and is reduced to the maximum of the security load in order to ensure the production safety. The concrete formula is as follows:
wherein the content of the first and second substances,
to ensure the safety load of safety production.
(4) Peak shaving rate
The peak regulation rate refers to the regulation rate when the user regulates the electricity consumption to a stable electricity consumption amount through response measures in the peak electricity consumption period. The concrete formula is as follows:
wherein
Is->
90%, (iv) is selected>
Is->
By the time of the peak load of the user, <' >>
For a user peak load &>
The time taken.
1.2 Potential error index
(1) Rate of fluctuation
The fluctuation rate reflects the fluctuation of the load curve, which reflects the degree of dispersion of the load on a time scale. The larger the fluctuation rate is, the larger the fluctuation of the load curve of the user is, the stronger the reliability of time staggering and peak staggering is, and the larger the corresponding potential is. The concrete formula is as follows:
wherein the content of the first and second substances,
the variance of a typical daily load curve of a user; />
The average value of the loads of the typical daily load curve of the user.
(2) Load in time
The time-staggered load refers to the planned reduction of the power consumption in the peak period of the power consumption, and the power consumption load in the peak period of the whole network can be effectively reduced by time-staggered. The concrete formula is as follows:
wherein the content of the first and second substances,
the load value is the user load value in the peak period of the whole province; />
， />
Respectively in advance for the peak hours of the user>
Hour and delay of T 2 The value of the user load in an hour, device for selecting or keeping>
Determined according to the peak hour duration.
(3) Peak to valley difference rate
The peak-to-valley difference rate refers to the proportion of the difference between the maximum load and the minimum load of a typical daily load curve of a user to the maximum load. The concrete formula is as follows:
wherein the content of the first and second substances,
respectively the maximum load and the minimum load of a typical daily load curve of a user.
(4) Peak power consumption ratio
The peak power consumption ratio is used for describing a typical daily load curve of a user, and the specific gravity of the total power consumption amount occupied by the power consumption in the peak power consumption period is as follows:
wherein the content of the first and second substances,
for peak power consumption by the subscriber>
Typical daily electricity consumption for the user.
1.3 index of alternate rest potential
The power of the alternate break potential index is based on the alternate break load
And/or duty-off load falling rate>
And the cost per unit of electric quantity->
Based on the unit electric quantity contribution degree>
And (4) forming.
(1) Load of alternate break
For users with weekly rest schedules, the rest time is saturday and sunday, the load on weekends is reduced compared with the load on working days, and the reduced value is defined as the duty of the alternate rest. The duty of the alternate break is an absolute value, and the larger the value is, the more obvious alternate break potential is shown, and the alternate break should be preferentially arranged. The concrete formula is as follows:
wherein the content of the first and second substances,
for the user's duty-day load average value, </>>
The average value of the load of the user at the end of the week.
(2) Load reduction rate of alternate rest
The duty-down rate of the alternate break reflects the degree of the weekend load reduction compared with the weekday load reduction, and is a relative value reflecting the potential of the alternate break. Likewise, the larger the duty-down rate of the break-over indicates that the features of the break-over are more prominent for the user. The concrete formula is as follows:
1.4 cost per unit of electricity
The implementation of load control inevitably causes certain economic losses, and the loss values of different users are different. In order to improve the economic efficiency as much as possible, users with low cost per unit of electricity should be preferentially arranged to participate in the load control. The unit electricity cost calculation formula is as follows:
wherein
Produce a total value for the user's year, and>
is the total annual electricity consumption of the user,
1.5 degree of contribution of unit electric quantity
The unit electric quantity contribution degree considers two aspects of user benefit and environmental protection. Generally speaking, enterprises with low benefit and poor environmental protection should prioritize load regulation. The benefits mainly reflect the contribution of user production activities to the society, the environmental protection mainly reflects the influence of the user production activities on the ecological environment, and the calculation formula is as follows:
wherein
For annual revenue, < '> or <' > for a user>
The annual pollutant emission of users mainly comprises SO2, NOx and the like.
In the step S3, a demand response resource library is selected according to the evaluation method to perform a demand response potential test, and an evaluation result is output, where the example steps are as follows:
two aspects of characteristics of meeting peaks and avoiding peaks are extracted from a load resource library, and the selected parameters include peak time average load difference coefficient, peak avoiding load, peak regulation rate, fluctuation rate, time staggered load and peak-valley difference rate, and are subjected to normalized calculation. And calculating the weight of each parameter by using an entropy weight method after the corresponding parameter value is calculated, (see table 2), and finally evaluating by using TOPSIS (technique for order preference by similarity to Ideal solution for solution) to determine the adjustable potential of the flexible response resource according to the score.
TABLE 2 entropy weight method for calculating weights of industrial and commercial user influence parameters
The method is characterized in that a special transformer customer power supply service station user, a high-voltage meter reading class user, 5 factories, namely a customer directly belonging to a certain power supply company and a user directly belonging to a certain new city power supply company, are selected from industrial large users represented by steel, and the TOPSIS scores are sequentially from high to low: the system comprises a high-voltage meter reading class direct user (0.838), a new city power supply station direct user (0.755), a special transformer customer power supply service station direct user (0.528), a power supply company direct user (0.230) and a power supply company direct user (0.206).
In commercial building users, 5 companies, namely a certain shopping square limited company in Enshi, three property management limited liability companies in Wuhan and a certain shopping square limited company in Xifeng county, are selected for evaluation, and finally the TOPSIS scores are sequentially from high to low: wuhan's certain property management, inc. (0.568), enshi's certain shopping mall, inc. (0.515), wuhan's certain property management, inc. (0.478), xiandongcounty, certain shopping mall, inc. (0.468), wuhan's certain property management, inc. (0.294).
Based on the working practice, the method for quantitatively verifying and testing the utility of the flexible resource characteristics of the demand response is provided, the test and evaluation methods of loads of large-scale industrial users and commercial building users are divided, the rating characteristic indexes of each load are set, quantitative evaluation is respectively carried out according to the set indexes, and finally the demand response resource library is selected according to the evaluation result to preferentially carry out the demand response.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (5)
1. A method for verifying and testing utility quantification of flexible resource characteristics in demand response is characterized by comprising the following specific steps:
s1, establishing a load potential quantification model, extracting power utilization common characteristics of users, and forming a quantitative evaluation load response potential index detail;
s2, selecting loads of large industrial users and commercial building users according to the power utilization common characteristics of the users and the task requirements of demand response, setting a rating characteristic index of each load, and performing quantitative evaluation according to the set indexes to form a test evaluation method;
and S3, selecting a demand response resource library according to the evaluation method to carry out a demand response potential test, and outputting an evaluation result.
2. The method for utility quantification verification testing of demand response flexible resource characteristics as claimed in claim 1, wherein the load potential quantification model is established by approximating an ideal solution ordering TOPSIS method in the step S1.
3. The demand response flexible resource characteristic utility quantification verification test method as claimed in claim 1, wherein in the step S2, a demand response potential test evaluation method of a large industrial user load and a commercial building user load is divided according to a user power utilization commonality characteristic and a task requirement of demand response.
4. The method for quantitatively verifying and testing the utility of the demand response flexible resource characteristics as claimed in claim 1, wherein in the step S2, a rating characteristic index is set for each load, a multi-time scale flexible resource response potential index system is constructed from 3 aspects of time staggering potential, alternate break potential and peak avoidance potential, an industrial and commercial enterprise adjustable potential evaluation model including operating characteristics and economic characteristics is constructed, and the set indexes quantitatively evaluate the three loads respectively.
5. The method for the validation and testing of the utility quantification of the characteristic of the demand response flexible resource according to claim 1, wherein the evaluation result in the step S3 is realized by the following method:
acquiring input operation data of a designated area, and performing data cleaning on original data;
obtaining feature-fused operation data according to the cleaned data, and calculating indexes of large-scale industrial users and commercial building users by using the operation data;
and finally, evaluating by adopting TOPSIS, determining the adjustable potential of the flexible response resource according to the score, and quantitatively testing the characteristic index of the flexible resource and the response potential of a typical demand scene.
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| CN116151032A (en) * | 2023-04-17 | 2023-05-23 | 湖南大学 | Residential building dynamic load flexible potential calculation method, device, equipment and medium |
| CN116151032B (en) * | 2023-04-17 | 2023-07-14 | 湖南大学 | Residential building dynamic load flexible potential calculation method, device, equipment and medium |
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