CN113361960A - Large-scale demand response capacity quantification method, medium, device and equipment - Google Patents
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Abstract
The invention discloses a large-scale demand response capacity quantification method, medium, device and equipment. In order to realize large-scale generalization capability evaluation, the relation between the power consumption of small sample users and the inflection point of the response dead zone and the inflection point of the saturation zone of the small sample users is mined based on principal component analysis, the acquisition of key response parameters of general users in the large-scale generalization process is realized by adopting a mode of combining commonality and individuality, and finally, the large-scale demand response capability evaluation based on small sample data is realized.
Description
Technical Field
The invention belongs to the technical field of power grid engineering, and particularly relates to a large-scale demand response capacity quantification method, medium, device and equipment.
Background
Compared with a supply side, the demand side resource has the characteristics of individuality, dispersity, uncertainty and the like, accurate modeling of the demand side resource needs to depend on a high-density acquisition terminal, but wide-area dispersity of the demand side resource enables a data model acquired by small-range demonstration engineering construction to be difficult to popularize to a large-range large-capacity customer group, and with establishment of a double-carbon target and advancing of virtual power plant construction, demand response capability assessment work based on small-sample demand response characteristic modeling and having large-scale generalization capability has important significance for planning and operation of a power system. Accordingly, we propose a large-scale demand response capability quantification method, medium, apparatus and device.
Disclosure of Invention
The invention provides a large-scale demand response capacity quantification method, medium, device and equipment for realizing large-scale demand response capacity assessment, which can accurately model the demand response characteristics of small sample users in a demonstration engineering range for a dispatcher on one hand, and can evaluate the response capacity of a large-scale large-capacity client group after the demonstration engineering range is expanded in a future scene for a planner on the other hand.
The purpose of the invention can be realized by the following technical scheme: a large-scale demand response capacity quantification method comprises the following steps:
step 2, calculating a reference value and a fuzzy value of the linear region random parameter of the industry response model as a common characteristic of the industry user response model;
step 3, acquiring a daily power utilization curve of a test point user, calculating a power utilization characteristic index value of the test point user, and obtaining a relation between a response model determination parameter and a power utilization characteristic based on principal component analysis and least square fitting;
step 4, collecting daily power utilization curves of general users in the industry, calculating power utilization characteristic index values of the general users, and combining the relation between the industry determination parameters and the power utilization characteristic indexes to obtain determination parameter values of the users as the individual characteristics of the user response characteristics;
and 5, combining the industry common characteristics and the user individual characteristics to obtain a response characteristic modeling of a general user 'dead zone + linear zone + saturated zone' based on a small sample of the trial user, and realizing large-scale response capability quantification.
Further, the step 1 specifically comprises:
(1) response model
The response rate of the user's participation in demand response versus the stimulus intensity is generally described as a piecewise linear function based on the psychological tendency of the consumer to achieve the maximum available energy value at the minimum cost, as shown in FIG. 1. In fig. 1, a is a dead zone inflection point of the excitation intensity, B is a saturation zone inflection point of the excitation intensity, and a black curve is a boundary of the fluctuation range of the responsivity. As the excitation intensity increases, the user response behavior may experience a change from a dead zone to a linear zone to a saturated zone. When the excitation intensity is in a dead zone and a saturated zone, the response randomness of a user is small; when the excitation intensity is in a linear region, the randomness of the response of the user exhibits a law of increasing first and then decreasing.
The user is generally considered to have obvious random response only in a linear region, so the invention models the relevant parameters of the dead zone and the saturated region into determined parameters, including the abscissa r of the dead zone inflection point1Abscissa r of inflection point of saturation region2And ordinate r3. For the linear region, in order to accurately describe the uncertain set of line region user response, the invention models the response curve of the linear region as a quadratic function, if the inflection point (r) of the dead region is known10) and inflection point (r) of saturation region2,r3) And obtaining a response characteristic of a linear region:
will r is4Modeling as a random parameter, then according to r4The scribable areas respond to random characteristics. Therefore, the user demand response model is finally determined by the determined parameter r1、r2And r3And a random parameter r4And (4) jointly constructing.
(2) Moment uncertain distribution robust random parameter modeling
The response of the power consumer in the linear region has larger randomness, and the invention adopts the distribution robust optimization to model the random parameters of the linear region. For a certain industry, the distribution characteristics of random parameters of the industry cannot be directly obtained due to the lack of historical response data, and the historical response distribution of users on the basis of industry test points is considered to represent the random parameter distribution of the whole industry. The invention considers that the random parameter of the linear response region of the user meets the normal distribution, and determines a fuzzy set based on a box type method, which can be expressed as follows:
where δ is the excitation intensity, r1,r2,r3For a known determined parameter, r4Satisfy the normal distribution with mean value of mu and standard deviation of sigma0、σ0The mean and variance are the reference values. Δ μ and Δ σ are deviations of the mean and the variance. Mu.se、σeAre fuzzy values of mean and variance. For a given excitation intensity
The user response rate satisfies:
further, the concepts of the reference value and the fuzzy value of the linear region random parameter of the business response model in the step 2 are as follows:
for modeling the response characteristics of a general user in the industry, the invention considers that the determined parameters in the response model are related to the power consumption capacity and the power consumption law of the user and have stronger individual characteristics, and the random parameters representing the law of the linear region are related to the overall characteristics of the industry, so that the random parameters are modeled as the common characteristics of the industry. For a common user in a certain industry, the random parameter is represented by using moment uncertain distribution robustness, and the key points are the values of mean value and variance:
in the formula (I), the compound is shown in the specification,
mean value and square value satisfied by general users in the industryThe value of the difference reference is,
is the industry random parameter mean and variance fuzzy value. The linear region random parameters of users in different industries have different reference values and fuzzy values. The invention defines the value of the random parameter of the general users in the industry according to the distribution of the linear region parameter of the industry trial point users. Respectively calculating the mean values of random parameters of different trial users in the industry, calculating the mean value and standard deviation of the mean values, and defining the reference value and the fuzzy value of the mean value of the random parameters of the general users in the corresponding industry according to the mean values, namely:
μithe average value of the random parameters of the ith test point user in the industry is obtained. The same approach is taken for the variance, namely:
σiand (4) the variance of random parameters of the ith test point user in the industry.
Further, the step 3 specifically comprises:
(1) characteristic index of electricity consumption
The demand response characteristic of the power consumer is closely related to the daily power utilization curve of the power consumer, so that the power utilization characteristic index set is constructed by the method, and the internal relation between the user response characteristic parameter and the daily power utilization is mined. Considering that the power consumption of the user has seasonal characteristics, a seasonal power consumption characteristic index set is constructed, as shown in attached table 1.
(2) Index dimensionality reduction based on principal component analysis
It is considered that the indexes in the constructed index set are more and there is duplicated information, that is, the correlation between the indexes is larger. In order to effectively extract the power utilization information, the power utilization characteristic index set is subjected to dimension reduction based on a principal component analysis method. Several independent main components are extracted to ensure the total contribution rate to over 90% and the power consumption characteristics
The relationship of the index set is as follows:
wherein, UjRepresents the extracted principal component of the power consumption curve, lambdajRepresents the contribution ratio of the jth principal component,
coefficient, x, corresponding to i-th index for j-th principal componentiIs the value of the ith index. The values of the principal components are calculated respectively, and at this time, the extracted principal components are considered to be independent from each other and contain most information of the original index set.
(3) Least squares fit of response determining parameters to principal components
According to the response key parameters provided by the sample user, performing least square fitting between the response key parameters and the main components of the power utilization curve to obtain the following relation:
wherein the content of the first and second substances,
is a coefficient of the principal component.
The device comprises a tested user parameter acquisition module and a tested user response output module, wherein the tested user parameter acquisition module and the tested user response output module are configured to execute data processing in the method;
the measured user parameter acquisition module is used for acquiring the measured resource parameters of the target user; and the response output module of the tested user is used for inputting the tested resource parameters acquired by the tested user parameter acquisition module into a target decision model to obtain the response output of the tested user.
An electronic device comprising one or more processors, a system memory, a bus connecting the system memory and a processing unit, said processing unit executing data processing in the above method by running a program stored in the system memory.
A computer-readable storage medium, on which a computer program is stored, which computer program is executed by a processor to carry out the above-mentioned method.
The invention has the technical effects that: an uncertainty modeling method for power consumer participation demand response is provided; a large-scale user demand response capacity quantification method based on a small sample is provided; on one hand, the demand response characteristics of small sample users in the demonstration engineering range can be accurately modeled for scheduling personnel, and on the other hand, response capability of large-capacity customer groups in a large range after the demonstration engineering range is expanded in a future scene can be evaluated by planning personnel.
Drawings
FIG. 1 is a diagram of a demand response model based on consumer psychology;
FIG. 2 is a response characteristic uncertainty modeling diagram based on robust optimization;
FIG. 3 is a response characteristic uncertainty modeling diagram based on stochastic optimization;
FIG. 4 is a response characteristic uncertainty modeling diagram based on distributed robust optimization;
FIG. 5 is a schematic diagram of a demand response capability quantification determination apparatus;
FIG. 6 is a schematic diagram of a demand response capability quantifying apparatus;
Detailed Description
The present invention will be better understood and implemented by those skilled in the art by the following detailed description of the technical solution of the present invention with reference to the accompanying drawings and specific examples, which are not intended to limit the present invention.
The examples were carried out according to the procedure described in the present invention:
a large-scale demand response capacity quantification method comprises the following steps:
step 2, calculating a reference value and a fuzzy value of the linear region random parameter of the industry response model as a common characteristic of the industry user response model;
step 3, acquiring a daily power utilization curve of a test point user, calculating a power utilization characteristic index value of the test point user, and obtaining a relation between a response model determination parameter and a power utilization characteristic based on principal component analysis and least square fitting;
step 4, collecting daily power utilization curves of general users in the industry, calculating power utilization characteristic index values of the general users, and combining the relation between the industry determination parameters and the power utilization characteristic indexes to obtain determination parameter values of the users as the individual characteristics of the user response characteristics;
and 5, combining the industry common characteristics and the user individual characteristics to obtain a response characteristic modeling of a general user 'dead zone + linear zone + saturated zone' based on a small sample of the trial user, and realizing large-scale response capability quantification.
Wherein, step 1 includes:
(1) response model
The response rate of the user's participation in demand response versus the stimulus intensity is generally described as a piecewise linear function based on the psychological tendency of the consumer to achieve the maximum available energy value at the minimum cost, as shown in FIG. 1. In fig. 1, a is a dead zone inflection point of the excitation intensity, B is a saturation zone inflection point of the excitation intensity, and a black curve is a boundary of the fluctuation range of the responsivity. As the excitation intensity increases, the user response behavior may experience a change from a dead zone to a linear zone to a saturated zone. When the excitation intensity is in a dead zone and a saturated zone, the response randomness of a user is small; when the excitation intensity is in a linear region, the randomness of the response of the user exhibits a law of increasing first and then decreasing.
The user is generally considered to have obvious random response only in a linear region, so the invention models the relevant parameters of the dead zone and the saturated region into determined parameters, including the abscissa r of the dead zone inflection point1Abscissa r of inflection point of saturation region2And ordinate r3. For the linear region, in order to accurately describe the uncertain set of line region user response, the invention models the response curve of the linear region as a quadratic function, if the inflection point (r) of the dead region is known10) and inflection point (r) of saturation region2,r3) And obtaining a response characteristic of a linear region:
will r is4Modeling as a random parameter, then according to r4The scribable areas respond to random characteristics. Therefore, the user demand response model is finally determined by the determined parameter r1、r2And r3And a random parameter r4And (4) jointly constructing.
(2) Moment uncertain distribution robust random parameter modeling
The response of the power consumer in the linear region has larger randomness, and the invention adopts the distribution robust optimization to model the random parameters of the linear region. For a certain industry, the distribution characteristics of random parameters of the industry cannot be directly obtained due to the lack of historical response data, and the historical response distribution of users on the basis of industry test points is considered to represent the random parameter distribution of the whole industry. The invention considers that the random parameter of the linear response region of the user meets the normal distribution, and determines a fuzzy set based on a box type method, which can be expressed as follows:
where δ is the excitation intensity, r1,r2,r3For a known determined parameter, r4Satisfy the normal distribution with mean value of mu and standard deviation of sigma0、σ0The mean and variance are the reference values. Δ μ and Δ σ are deviations of the mean and the variance. Mu.se、σeAre fuzzy values of mean and variance. For a given excitation intensity
The user response rate satisfies:
the concepts of the reference value and the fuzzy value of the linear region random parameter of the business response model in the step 2 are as follows:
for modeling the response characteristics of a general user in the industry, the invention considers that the determined parameters in the response model are related to the power consumption capacity and the power consumption law of the user and have stronger individual characteristics, and the random parameters representing the law of the linear region are related to the overall characteristics of the industry, so that the random parameters are modeled as the common characteristics of the industry. For a common user in a certain industry, the random parameter is represented by using moment uncertain distribution robustness, and the key points are the values of mean value and variance:
in the formula (I), the compound is shown in the specification,
the mean and variance reference values satisfied by the general users in the industry,
mean and variance blur values. The linear region random parameters of users in different industries have different reference values and fuzzy values. The invention defines the value of the random parameter of the general users in the industry according to the distribution of the linear region parameter of the industry trial point users. Respectively calculating the mean value of random parameters of different trial users in the industry, calculating the mean value and standard deviation of the mean value, and defining the random parameters corresponding to the general users in the industryThe reference value and the fuzzy value of the parameter mean value are as follows:
μithe average value of the random parameters of the ith test point user in the industry is obtained. The same approach is taken for the variance, namely:
σiand (4) the variance of random parameters of the ith test point user in the industry.
Wherein, step 3 includes:
(1) characteristic index of electricity consumption
The demand response characteristic of the power consumer is closely related to the daily power utilization curve of the power consumer, so that the power utilization characteristic index set is constructed by the method, and the internal relation between the user response characteristic parameter and the daily power utilization is mined. Considering that the power consumption of the user has seasonal characteristics, a seasonal power consumption characteristic index set is constructed, as shown in attached table 1.
(2) Index dimensionality reduction based on principal component analysis
It is considered that the indexes in the constructed index set are more and there is duplicated information, that is, the correlation between the indexes is larger. In order to effectively extract the power utilization information, the power utilization characteristic index set is subjected to dimension reduction based on a principal component analysis method. Several independent main components are extracted to ensure the total contribution rate to over 90% and the power consumption characteristics
The relationship of the index set is as follows:
wherein, UjRepresents the extracted principal component of the power consumption curve, lambdajRepresents the contribution ratio of the jth principal component,
coefficients corresponding to the i-th index for the j-th principal component. The values of the principal components are calculated respectively, and at this time, the extracted principal components are considered to be independent from each other and contain most information of the original index set.
(3) Least squares fit of response determining parameters to principal components
According to the response key parameters provided by the sample user, performing least square fitting between the response key parameters and the main components of the power utilization curve to obtain the following relation:
wherein the content of the first and second substances,
is a coefficient of the principal component.
Example 1
And verifying the effectiveness of the method by responding to the actual load data of the users in the test point park in the demand of a certain area. The park is mainly in the electronic equipment manufacturing industry and the textile industry, has 28 users and 20 users respectively, and has continuously participated in a power grid demand response event for two years, the demand response event is mainly in price type demand response, and the power grid realizes the reduction of loads on the user side by applying peak-valley price difference. The load shedding rate at which users on the campus participate in the demand response event and their 96 point daily load data can be collected. In addition, campus users report dead zone inflection points (r) on demand every quarter10) and inflection point (r) of saturation region2,r3)。
1. Modeling individual park user linear zones
According to the invention, a representative user (hereinafter referred to as electronic user) is selected from the electronic equipment manufacturing industry to perform individual user regional modeling. Taking response data of the first quarter of the first year as an example, the data is usedR of family upload1,r2,r3Respectively 0.2,0.9 and 0.8, so that the dead zone inflection points of the points A and B in FIG. 2 are (0.2 and 0) and the saturation zone inflection points of the points B are (0.9 and 0.8), and the grid demand response events are participated in for 123 times in a quarter, wherein 98 times of data are in a linear region. Uncertainty modeling is performed on the linear region parameters of the electronic user based on the following three methods, and fig. 2-4 show the modeling results of the three methods.
M1 robust optimization uncertainty modeling
M2 stochastic optimization uncertainty modeling
M3 distributed robust optimization uncertainty modeling based on moment information
In fig. 2, the plus sign is marked as the historical response point of the electronic user for one quarter, the middle curve region is a linear region value-taking region obtained by M1, and the linear region characteristic can be represented as:
η＝r4(δ-0.2)(δ+1.14/r4-0.9)
where.-1.47≤r4≤1.52
the load reduction rate of the electronic user is a uniform distribution interval under the given peak-to-valley price difference. However, from the distribution of blue marks, most of the historical points of the user's response are concentrated in the middle of the uncertain domain, a few random points are distributed at the edge, and for the reduction rate at a given peak-to-valley price difference, uniform distribution cannot represent the distribution form with dense middle and sparse two sides.
M2 is based on the thought of random optimization, thinks that the random distribution of the linear region parameter of user satisfies the positive distribution rule, obtains its distribution function according to its historical response data, obtains the response characteristic:
η＝r4(δ-0.2)(δ+1.14/r4-0.9)
where.r4～N(-0.42,0.512)
fig. 3 shows the variation of the peak-to-valley price difference with the probability distribution of the electronic load reduction rate. It can be seen that the middle segment of the surface is relatively slow, corresponding to a large uncertainty of the user response, and as the peak-to-valley price difference is closer to the dead zone and the saturated zone, the user uncertainty is smaller, and the reduction rate probability distribution surface is more jittered. At a given peak-to-valley price difference, the cut rate of the e-user is a random quantity that satisfies a positive-too-distribution. M2 reasonably characterizes the distribution of this user compared to the uniform distribution interval of M1.
Further collecting response data of the electronic user in the first quarter of the second year, and obtaining the linear region characteristics based on M3 as follows:
η＝r4(δ-0.2)(δ+1.14/r4-0.9)
where.r4～N(-0.23,0.822)
obviously, the characteristics of the user in the first quarter of two years obtained based on M2 are different, the main reason is that the production cost of the power user mainly comes from power utilization, the power user is sensitive to excitation of demand response, most response points meet a certain rule from the perspective of historical response data, only a few discrete random points are sufficient, namely, data samples for representing randomness of the discrete random points are insufficient, and when M2 estimates the true distribution of random parameters directly based on moment information of the historical data, estimation errors are difficult to avoid.
M3 adopts the robust optimization based distribution to construct the parameter r of the linear region of the electronic user4Is considered as a parameter r4Satisfying normal distribution, but its moment information is not fixed, and the response characteristic of the linear region can be described as:
fig. 4 shows a change in the peak-to-valley price difference of the fuzzy set of probability distribution of the load reduction rate. Compared with fig. 3, it is no longer a probability distribution surface, but a probability distribution space. The user gets the reduction rate as a fuzzy set of probability distribution given the peak-to-valley price difference. The larger the fuzzy set constructed, the more robust it is. Compared with M2, the fuzzy set of M3 can effectively solve the problem of estimation error in M2.
By contrast, M1 does not consider data distribution information of response samples; m2 estimates the true probability distribution of the linear region parameters through the sample data, which can cause a certain estimation error; m3 constructs a fuzzy set of probability distribution of linear region parameters on the basis of M2, and has stronger robustness.
Example 2
Fig. 5 is a schematic diagram of a device for quantitatively determining demand response capability according to embodiment 2 of the present invention. The embodiment can be applied to the condition of quantifying the demand response capability of the target user, the device can be realized in a software and/or hardware mode, and the device can be configured in terminal equipment. The demand response capability quantification determination device includes: a measured user parameter acquisition module 410 and a measured user response output module 420.
The measured user parameter obtaining module 410 is configured to obtain a measured resource parameter of a target user;
and the measured user response output module 420 is configured to input the measured resource parameters of the target user into the target decision model, so as to obtain the response output of the measured user.
According to the technical scheme, the acquisition of key response parameters of general users in a large-scale generalization process is realized by adopting a mode of combining commonalities and individuality, and finally, the large-scale demand response capacity quantification based on small sample data is realized.
Example 3
Fig. 6 is a schematic structural diagram of an apparatus provided in embodiment 3 of the present invention, where the embodiment of the present invention provides a service for implementing the method for determining a demand response capability according to the above embodiment of the present invention, and may configure a device for determining a demand response capability according to the above embodiment. Fig. 6 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 6 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present invention.
As shown in FIG. 6, device 12 is in the form of a general purpose computing device. The components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement the demand response capability quantifying method provided by the embodiment of the present invention.
Through the equipment, the problem of large-scale demand response capacity quantification based on small sample data is solved.
Example 4
Embodiment 4 of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a large-scale user demand response capacity quantification calculation method, where the method includes:
acquiring a measured parameter of a target user;
and inputting the measured parameters into a pre-trained quantitative model of the demand response capability to obtain the output demand response capability of the target user.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the above method operations, and may also perform related operations in the determination method for quantifying the demand response capability provided by any embodiment of the present invention.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (7)
1. A large-scale demand response capacity quantification method is characterized by comprising the following steps:
step 1, collecting historical response data of each test point user, response model dead zones and saturation zone inflection points, modeling random parameters based on moment uncertain distribution robustness, and calculating the mean value and variance of the random parameters in a linear zone of a response model;
step 2, calculating a reference value and a fuzzy value of a random parameter in a linear region of the response model as a common characteristic of the industry user response model;
step 3, acquiring a daily power utilization curve of a test point user, calculating a power utilization characteristic index value of the test point user, and obtaining a relation between a response model determination parameter and a power utilization characteristic based on principal component analysis and least square fitting;
step 4, collecting a daily power utilization curve of a general user, calculating a power utilization characteristic index value of the general user, and combining the relation between the industry determined parameter and the power utilization characteristic index to obtain a determined parameter value of the user as an individual characteristic of the user response characteristic;
and 5, combining the industry common characteristics and the user individual characteristics to obtain a response characteristic modeling of a general user 'dead zone + linear zone + saturated zone' based on a small sample of the trial user, and realizing large-scale response capability quantification.
2. The large-scale demand response capacity quantification method according to claim 1, wherein the step 1 concrete modeling method is as follows:
(1) building response model
Describing the relation between the response rate of the user participating in the demand response and the excitation strength as a piecewise linear function, wherein the response behavior of the user is changed from a dead zone to a linear zone and then to a saturation zone along with the increase of the excitation strength, and the response randomness of the user is smaller when the excitation strength is in the dead zone and the saturation zone; when the excitation intensity is in a linear region, the response randomness of the user presents a rule of increasing first and then decreasing;
considering that the user only has obvious response randomness in a linear region, relevant parameters of a dead region and a saturated region are modeled as determined parameters, including an abscissa r of a dead region inflection point1Abscissa r of inflection point of saturation region2And ordinate r3(ii) a Modeling the linear region response curve as a quadratic function if the dead zone inflection point (r) is known10) and inflection point (r) of saturation region2,r3) And obtaining a response characteristic of a linear region:
will r is4Modeling as a random parameter, then according to r4The scribable linear region responds to the random characteristic, so that the user demand response model is finally determined by the determined parameter r1、r2And r3And a random parameter r4Constructing together;
(2) moment uncertain distribution robust random parameter modeling
Considering that the response of the power consumer in the linear region has larger randomness, modeling the linear region random parameters by adopting distribution robust optimization, considering that the user response linear region random parameters meet normal distribution, and determining a fuzzy set based on a box type method, wherein the method can be expressed as follows:
where δ is the excitation intensity, r1,r2,r3For a known determined parameter, r4Satisfy the normal distribution with mean value of mu and standard deviation of sigma0、σ0Is a reference value of the mean and the variance, and Δ μ and Δ σ are deviations of the mean and the variance, μe、σeFuzzy values for mean and variance, for a given excitation intensity
The user response rate satisfies:
3. the method as claimed in claim 1, wherein the method for calculating the probability of the reference value and the fuzzy value of the linear region random parameter of the response model in step 2 is as follows:
the moment uncertain distribution robustness is adopted to represent random parameters, and the values of the mean value and the variance are as follows:
in the formula (I), the compound is shown in the specification,
is full of common users in the industryThe mean and variance reference values of the feet,
the method comprises the following steps of defining values of random parameters of general users in the industry according to distribution of linear region parameters of users in the industry, respectively calculating mean values of the random parameters of the users in different industry, calculating average values and standard deviations of the mean values, and defining the reference values and the fuzzy values of the mean values of the random parameters of the corresponding users in the industry according to the mean values and the fuzzy values, namely:
μifor the mean value of random parameters of the ith test point user in the industry, the variance is processed in the same way, namely:
σiand (4) the variance of random parameters of the ith test point user in the industry.
4. The method for quantifying large-scale demand response capability according to claim 1, wherein the detailed contents in step 3 are as follows:
(1) characteristic index of electricity consumption
Building a power utilization characteristic index set, mining the internal relation between the user response characteristic parameters and the daily power utilization of the user, building a seasonal power utilization characteristic index set by considering the seasonal characteristics of the power utilization of the user,
(2) index dimensionality reduction based on principal component analysis
The method is characterized in that the power utilization characteristic index set is subjected to dimension reduction based on a principal component analysis method, a plurality of mutually independent principal components are extracted, the total contribution rate is ensured to reach more than 90%, and the relationship between the total contribution rate and the power utilization characteristic index set is as follows:
wherein, UjRepresents the extracted principal component of the power consumption curve, lambdajRepresents the contribution ratio of the jth principal component,
calculating the value of each principal component for the coefficient of the jth principal component corresponding to the ith index respectively, wherein the extracted principal components are considered to be independent from each other at the moment and contain most of information of the original index set;
(3) least squares fit of response determining parameters to principal components
According to the response key parameters provided by the sample user, performing least square fitting between the response key parameters and the main components of the power utilization curve to obtain the following relation:
wherein the content of the first and second substances,
is a coefficient of the principal component.
5. An apparatus, comprising a measured user parameter obtaining module (410) and a measured user response output module (420), wherein the measured user parameter obtaining module (410) and the measured user response output module (420) are configured to perform data processing in the method according to any one of claims 1-4;
the measured user parameter acquisition module (410) is used for acquiring the measured resource parameter of the target user; and the response output module (420) of the tested user is used for inputting the tested resource parameters acquired by the parameter acquisition module (410) of the tested user into a target decision model to obtain the response output of the tested user.
6. An electronic device comprising one or more processors (16), a system memory (28), a bus (18) connecting the system memory (280) and a processing unit (16), the processing unit (16) executing a program stored in the system memory (28) for performing the data processing in the method according to any of claims 1-5.
7. A computer-readable storage medium, having stored thereon a computer program for execution by a processor to perform the method of any one of claims 1-6.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107017630A (en) * | 2017-05-18 | 2017-08-04 | 南京师范大学 | A kind of electric power demand side response method of Multiple Time Scales |
| CN109409688A (en) * | 2018-09-29 | 2019-03-01 | 东南大学 | A kind of demand response effect towards interruptible load appraisal procedure stage by stage |
| CN110414804A (en) * | 2019-07-08 | 2019-11-05 | 华中科技大学 | A kind of price type demand response modeling method based on various dimensions response characteristic |
| CN112785184A (en) * | 2021-01-29 | 2021-05-11 | 四川大学 | Source network load coordination distribution robust long-term expansion planning method considering demand response |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107017630A (en) * | 2017-05-18 | 2017-08-04 | 南京师范大学 | A kind of electric power demand side response method of Multiple Time Scales |
| CN109409688A (en) * | 2018-09-29 | 2019-03-01 | 东南大学 | A kind of demand response effect towards interruptible load appraisal procedure stage by stage |
| CN110414804A (en) * | 2019-07-08 | 2019-11-05 | 华中科技大学 | A kind of price type demand response modeling method based on various dimensions response characteristic |
| CN112785184A (en) * | 2021-01-29 | 2021-05-11 | 四川大学 | Source network load coordination distribution robust long-term expansion planning method considering demand response |
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