CN112862535A - Method for determining power price responsiveness of power-dedicated transformer client and terminal equipment - Google Patents
Method for determining power price responsiveness of power-dedicated transformer client and terminal equipment Download PDFInfo
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Abstract
The invention is suitable for the technical field of power grids, and discloses a method for determining the power price responsiveness of a power special transformer customer and a terminal device, wherein the method comprises the following steps: acquiring historical electricity load data and electricity utilization equipment of a power special transformer client, and acquiring electricity price data; based on historical electricity load data and electricity utilization equipment, carrying out load decomposition on the electricity special transformer customer to obtain the use distribution information of each electricity utilization equipment of the electricity special transformer customer; and determining the electricity price responsivity of the electricity special change customer according to the use distribution information and the electricity price data of each electricity utilization device of the electricity special change customer. The method can comprehensively determine the power price responsivity of the power special transformer client, and can improve the accuracy of the power price responsivity of the power special transformer client.
Description
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a method for determining power price responsiveness of a power special transformer customer and terminal equipment.
Background
The power special transformer client is a power client adopting a special transformer power supply mode and has the characteristics of small quantity and large power consumption. The power supply enterprise can excavate the flexible interaction potential of the special transformer customer by calculating the power price responsiveness of the special transformer customer, so that the power supply enterprise is guided to actively adjust the load and participate in power grid peak shaving, and the safety and the stability of the power grid are improved.
Currently, the calculation of the electricity price responsiveness is generally performed based on the total load of the customers, but the determination of the electricity price responsiveness of the specific customer by only the total load of the customers is one-sided, and the result is inaccurate.
Disclosure of Invention
In view of this, the embodiment of the invention provides a method for determining power price responsiveness of a power-specific transformer client and a terminal device, so as to solve the problem that in the prior art, the power price responsiveness of the power-specific transformer client is determined only by the total load of the client, and the result is inaccurate.
The first aspect of the embodiment of the invention provides a method for determining the responsivity of the price of electricity of a power special transformer customer, which comprises the following steps:
acquiring historical electricity load data and electricity utilization equipment of a power special transformer client, and acquiring electricity price data;
based on historical electricity load data and electricity utilization equipment, carrying out load decomposition on the electricity special transformer customer to obtain the use distribution information of each electricity utilization equipment of the electricity special transformer customer;
and determining the electricity price responsivity of the electricity special change customer according to the use distribution information and the electricity price data of each electricity utilization device of the electricity special change customer.
A second aspect of an embodiment of the present invention provides a power rate responsivity determining apparatus for a power specific transformer customer, including:
the acquisition module is used for acquiring historical electricity load data and electricity utilization equipment of the power special transformer client and acquiring electricity price data;
the load decomposition module is used for carrying out load decomposition on the power special transformer client based on historical power load data and power utilization equipment to obtain the use distribution information of each power utilization equipment of the power special transformer client;
and the electricity price responsivity determining module is used for determining the electricity price responsivity of the electricity special transformer customer according to the use distribution information and the electricity price data of each electric device of the electricity special transformer customer.
A third aspect of embodiments of the present invention provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the power special change customer electricity price responsiveness determining method according to the first aspect when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by one or more processors, implements the steps of the power rate responsiveness determining method for a power specific transformer customer as described in the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: according to the embodiment of the invention, the historical electricity load data and the electricity utilization equipment of the electricity special transformer client are firstly obtained, the electricity price data is obtained, then the load decomposition is carried out on the electricity special transformer client based on the historical electricity load data and the electricity utilization equipment to obtain the use distribution information of each electricity utilization equipment of the electricity special transformer client, and finally the electricity price responsivity of the electricity special transformer client is determined according to the use distribution information and the electricity price data of each electricity utilization equipment of the electricity special transformer client, so that the electricity price responsivity of the electricity special transformer client can be comprehensively determined, and the accuracy of the electricity price responsivity of the electricity special transformer client can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart illustrating an implementation process of a power price responsiveness determining method for a power specific transformer customer according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an initial load model of power load of a power specific customer according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a power rate responsivity determining apparatus for a power specific customer according to an embodiment of the present invention;
fig. 4 is a schematic block diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart of an implementation of a power price responsiveness determining method for a power specific transformer customer according to an embodiment of the present invention, and for convenience of description, only a part related to the embodiment of the present invention is shown. The execution main body of the embodiment of the invention can be terminal equipment. As shown in fig. 1, the method may include the steps of:
s101: historical electricity load data and electric equipment of the power special transformer client are obtained, and electricity price data are obtained.
Wherein, the power special transformer customer can be a 10kV power special transformer customer. In the embodiment of the invention, the power price responsivity can be calculated for the power special change customers in a certain power supply area.
Specifically, for a 10kV power-dedicated customer in a certain power supply area, historical power load data of the customer is obtained, specifically, the historical power load data may be power loads for N consecutive months (where, the power load data for the previous D days may be used for training of a subsequent load model, and the power load data for the remaining R days may be used for determining the power price responsiveness of the customer), and is recorded as O {1,O2,...,OD+R}. Wherein, Oi(i ═ 1, 2., D + R) is the electricity load data of the customer on the ith day, which can be specifically represented as Oi{o1,o2,...,oT}; the value of T is set according to the requirement, and if the sampling period is 1 hour, T is 24; if the sampling period is 15 minutes, T is 96. otThe electric load at time t.
It should be noted that, for convenience of representation, when the electricity load of a specific customer in a day is modeled in a refined manner, O is omitted on the premise that no ambiguity is causediSubscript of (a) is represented by O { O }1,o2,...,oT}。
The electric equipment of the power-dedicated customer can be the main electric equipment of the customer. The electricity price data can be the electricity price data of a typical day of the power supply station area and is recorded as gamma1,γ2,...,γT. If time-of-use pricing is adopted, then the gamma values are differentkThe values of (A) are different; otherwise, γ1＝γ2＝...＝γT。
S102: and performing load decomposition on the power special-purpose transformer customer based on the historical power load data and the power utilization equipment to obtain the use distribution information of each power utilization equipment of the power special-purpose transformer customer.
In an embodiment of the present invention, the S102 may include:
step 2.1: and modeling the power load of the power special change customer based on the hidden Markov model to obtain an initial load model.
Specifically, referring to fig. 2, fig. 2 is an initial load model of the power load of the power specific customer, which is defined as follows:
S{s1,s2,...,sT}: the state sequence represents the working process of each electric equipment of the power special transformer customer;
O{o1,o2,...,oT}: representing the total electrical load sequence observed at the entrance of the power specific customer.
The observation sequence can be obtained through an intelligent measuring instrument at the entrance of the special transformer client, and the state sequence cannot be obtained through observation.
The hidden Markov model construction includes two basic assumptions:
(1) homogeneous markov assumption: the state at any time t of the state sequence depends only on the state at the previous time, and is independent of the states or observations at other times, namely:
P(st|st-1,ot-1,st-2,ot-2,...,s1,o1)＝P(st|st-1)t＝1,2,...,T
(2) observation independence hypothesis: the observation at any time is only related to the state at the current time, but not to the state or observation at other times, i.e.:
P(ot|sT,oT,...,s1,o1)＝P(ot|st)t＝1,2,...,T
assuming that all the electric devices only contain an integer number of states, the state space capacity of the initial load model is the product of the number of states contained in each electric device. Recording the state space of the established initial load model as Q { Q }1,q2,...,ql}。
The model parameters of the initial load model are θ (π, A, φ), where:
pi is the probability of the initial state, the element pii＝P(s1＝qi)i＝1,2,...,l；
A is a state transition matrix, the element a of whichij＝P(st＝qj|st-1＝qi)i,j＝1,2,...,l；t＝1,2,...,T；
Phi is the output probability, its element bi(ot)＝P(ot|st＝qi) Generally described by a Gaussian distribution, i.e.
Wherein the content of the first and second substances,
respectively represent states qiCorresponding to the mean and variance of the gaussian distribution.
Step 2.2: based on an EM (expectation maximization) algorithm, training an initial load model according to historical power load data to obtain a final load model.
Specifically. According to historical electricity load data of the power special transformer client, an EM algorithm is used for training to obtain parameter estimation of an initial load model, namely theta (pi, A, phi). In the embodiment of the present invention, the initial load model after the parameter training is referred to as a final load model.
The electric load data of D days before the historical electric load data is used as training data, and the electric load data of R days after the historical electric load data is used as test data.
The method comprises the following specific steps:
1) inputting: training data { O1,O2,...,OD}；
2) Initializing theta0Let k be 0;
3) e, step E: calculating the expectation of the log-likelihood function:
4) and M: calculating the model parameters that maximize the log-likelihood function expectation:
5) if thetak+1-θkIf | < epsilon, the algorithm converges, the iteration ends, and the output theta is equal to thetak+1(ii) a Otherwise, it ordersk is k +1, and the process returns to the step 3).
Wherein epsilon is an extremely small number, is used for judging algorithm convergence, and can be selected to be 10 according to actual needs-1、10-2、10-3And the like.
Step 2.3: and based on a Viterbi algorithm, carrying out load decomposition according to the final load model to obtain a power consumption state sequence set.
Specifically, first, the power load data of a typical day of the power special change client is acquired, and the power state sequence of the typical day of the power special change client is obtained by using the Viterbi algorithm according to the power load data of the typical day.
The essential variables are defined as follows:
defining the state at time t as qi(qiE.q) of all individual paths(s)1，s2，...，st) The maximum value of the probability of (1) is:
by definition, δ can be obtainedtThe recurrence formula of (c) is:
defining the state at time t as qi(qiE.q) of all individual paths(s)1，s2，...，st) The t-1 node of the path with the highest probability in (1) is:
wherein, ajiIs an element of the state transition matrix; l is the total number of states in the state space.
The algorithm comprises the following specific steps:
1) inputting: final load model parameters θ (π, A, φ), observation sequence O { O1，o2，...，oT}。
2) Initializing a local state:
δ1(i)＝πibi(o1)；
wherein, delta1(i) State s when t is 11＝qi(qiE.g. Q) and observed as o1The probability of (c).
3) And recursion is carried out, and the local state at each moment is obtained:
4) calculating delta at which time T is maximumT(i) And psiT(i)：
Wherein, P*Is the probability that the most likely sequence of states occurs,
the most likely state at time T.
5) Optimal state backtracking:
6) obtaining the most likely state sequence
Then theAll test data for power specific customers OD+1,OD+2,...,OD+RCalculating corresponding power utilization state sequence set { S) by adopting the method1,S2,...,SR}。
Step 2.4: and determining the use distribution information of each electric device according to the electricity utilization state sequence set.
In the embodiment of the invention, the existing method can be adopted to summarize the use distribution information of each electric equipment of the client according to the electricity utilization state sequence set of the power special transformer client, and the use probability of each electric equipment of the client at the time T (T is more than or equal to 1 and less than or equal to T) can be obtained.
Assuming that the total number of the electric equipment of the power-dedicated customer is m, the use probability of the electric equipment i at the time t is recorded as pi,t(1≤i≤m,1≤t≤T)。
S103: and determining the electricity price responsivity of the electricity special change customer according to the use distribution information and the electricity price data of each electricity utilization device of the electricity special change customer.
In an embodiment of the present invention, the S103 may include:
calculating the electricity price responsivity of each electric device of the power special transformer customer according to the use distribution information and the electricity price data of each electric device of the power special transformer customer;
and determining the average value of the power rate responsivity of each electric device of the power special transformer client as the power rate responsivity of the power special transformer client.
In one embodiment of the present invention, calculating the electricity price responsivity of each electricity consumption device of the electricity specific customer according to the usage distribution information and the electricity price data of each electricity consumption device of the electricity specific customer includes:
aiming at each electric equipment of the electric power special transformer customer, calculating the value of each parameter of the electric equipment according to the use distribution information and the electricity price data of the electric equipment, and carrying out standardization processing on the value of each parameter of the electric equipment by adopting a range conversion method to obtain the standard value of each parameter of the electric equipment;
determining the weight of each parameter of the electric equipment based on an entropy weight method;
and determining the electricity price responsivity of the electric equipment according to the standard value of each parameter of the electric equipment and the weight of each parameter of the electric equipment.
In one embodiment of the invention, the parameters include a variance of the power usage distribution, a gray degree correlation of the power usage distribution to the negative value of the power rate, and a weighted average of the power rates at the same time with respect to the probability of use of the power consumer.
In one embodiment of the present invention, the usage distribution information of each electric device of the power specific transformation client includes a usage probability of each electric device of the power specific transformation client at each time;
the grey correlation degree of the power consumption distribution and the electricity price negative value is calculated by the formula
The calculation formula of the weighted average of the electricity price at the same time and the use probability of the electric equipment is
Wherein T represents the total number of sampling instants within a day; m represents the number of electric devices; gamma raytRepresents the electricity price at time t; p is a radical ofi,tIndicating the probability of use of the powered device i at time t.
Specifically, for each electric device of the power exclusive customer, a value of a variance of the electric power consumption distribution, a value of a gray correlation degree of the electric power consumption distribution with an electric power price negative value, and a value of a weighted average of the simultaneous electric power prices with respect to the electric device usage probability are calculated. Wherein, the variance of the power distribution can be calculated by adopting the existing method, and the variance can embody the fluctuation. The grey correlation degree of the power utilization power distribution and the power utilization price negative value can represent the similarity of the power utilization prices, and meanwhile, the weighted average of the power utilization price on the power utilization equipment use probability can represent the converted power utilization price.
According to the properties of the three parameters, the variance of the power consumption power distribution and the grey correlation degree of the power consumption power distribution and the electricity price negative value can be used as forward parameters, namely, the larger the parameter value is, the better the parameter value is; the weighted average of the simultaneous electricity prices with respect to the probability of use of the consumers can be classified as a negative-going parameter, i.e. the smaller the parameter value the better.
And calculating the values of the three parameters for each electric device of the power special transformer customer, and then standardizing the values of the three parameters by adopting a range transform method.
Specifically, the values of the parameters are normalized by a range transform method, so that homotrenization and dimensionless transformation are realized. Establishing a decision matrix X ═ (X)ij)m×nWherein x isijThe value of the jth parameter of the ith powered device is represented. Normalizing the parameter values using the following equation to obtain a normalized decision matrix Y ═ Yij)m×n。
1) For the forward type parameter, the larger the value is, the better the value is:
in the formula (I), the compound is shown in the specification,
the minimum and maximum values that can be obtained for the jth parameter are shown in the table.
2) For negative-going parameters, the smaller the value, the better the value:
and determining the weight of each parameter of the electric equipment based on an entropy weight method aiming at each electric equipment of the electric power special change customer. The method comprises the following specific steps:
1) calculating the specific gravity p of the jth parameter of the ith electric equipmentij：
2) Calculating the entropy e of the jth parameterj：
In particular, when pijWhen equal to 0, pij ln pij＝0。
3) Calculating the difference coefficient g of the jth parameterj：
gj＝1-ej j＝1,2,...,n
4) Calculating the weight v of the jth parameterj：
Determining the electricity price responsivity of each electric device according to the standard value of each parameter of each electric device and the weight of each parameter of each electric device
The set of electricity price responsibilities of the consumers of the power specialization may be expressed as Z ═ Z (Z ═ Z)i)m。
Averaging the power price responsivities of all the electric equipment of the same power special transformer client to obtain the power price responsivity lambda of each power special transformer clienti(i ═ 1, 2.., k). Where k is the total number of power specific customers.
As can be seen from the above description, in the embodiment of the present invention, the historical power consumption load data and the power consumption devices of the power-dedicated customer are first obtained, the power price data is obtained, then, the load decomposition is performed on the power-dedicated customer based on the historical power consumption load data and the power consumption devices, the use distribution information of each power consumption device of the power-dedicated customer is obtained, and finally, the power price responsivity of the power-dedicated customer is determined according to the use distribution information and the power price data of each power consumption device of the power-dedicated customer, so that the power price responsivity of the power-dedicated customer can be comprehensively determined, and the accuracy of the power price responsivity of the power-dedicated customer can be improved.
The embodiment of the invention can identify the equipment-level load characteristics of the power special transformer client and realize the fine determination of the power price responsiveness of the power special transformer client.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Corresponding to the method for determining the responsiveness of the electricity price of the power special transformer client, an embodiment of the invention also provides a device for determining the responsiveness of the electricity price of the power special transformer client. Fig. 3 is a schematic block diagram of a power rate responsivity determining apparatus for a power specific customer according to an embodiment of the present invention, and for convenience of explanation, only a part related to the embodiment of the present invention is shown.
In an embodiment of the present invention, the power specific customer electricity price responsiveness determining device 30 may include an obtaining module 301, a load splitting module 302, and an electricity price responsiveness determining module 303.
The acquisition module 301 is configured to acquire historical power load data and power consumption equipment of a power-dedicated customer, and acquire power price data;
the load decomposition module 302 is used for performing load decomposition on the power special transformer customer based on historical power load data and power equipment to obtain the use distribution information of each power equipment of the power special transformer customer;
and the electricity price responsivity determining module 303 is configured to determine the electricity price responsivity of the electricity special transformer customer according to the usage distribution information and the electricity price data of each electricity utilization device of the electricity special transformer customer.
Optionally, the load decomposition module 302 is specifically configured to:
modeling the power load of the power special transformer customer based on a hidden Markov model to obtain an initial load model;
training an initial load model according to historical power load data based on an EM algorithm to obtain a final load model;
based on a Viterbi algorithm, carrying out load decomposition according to a final load model to obtain a power consumption state sequence set;
and determining the use distribution information of each electric device according to the electricity utilization state sequence set.
Optionally, the electricity price responsivity determining module 303 is specifically configured to:
calculating the electricity price responsivity of each electric device of the power special transformer customer according to the use distribution information and the electricity price data of each electric device of the power special transformer customer;
and determining the average value of the power rate responsivity of each electric device of the power special transformer client as the power rate responsivity of the power special transformer client.
Optionally, the electricity price responsivity determining module 303 may be further configured to:
aiming at each electric equipment of the electric power special transformer customer, calculating the value of each parameter of the electric equipment according to the use distribution information and the electricity price data of the electric equipment, and carrying out standardization processing on the value of each parameter of the electric equipment by adopting a range conversion method to obtain the standard value of each parameter of the electric equipment;
determining the weight of each parameter of the electric equipment based on an entropy weight method;
and determining the electricity price responsivity of the electric equipment according to the standard value of each parameter of the electric equipment and the weight of each parameter of the electric equipment.
Optionally, the parameters include a variance of the power usage distribution, a grey correlation of the power usage distribution with a negative value of the electricity price, and a weighted average of the power usage probability with respect to the electricity usage at the same time.
Optionally, the usage distribution information of each electric device of the power-dedicated transformation client includes a usage probability of each electric device of the power-dedicated transformation client at each time;
the grey correlation degree of the power consumption distribution and the electricity price negative value is calculated by the formula
The calculation formula of the weighted average of the electricity price at the same time and the use probability of the electric equipment is
Wherein T represents the total number of sampling instants within a day; m represents the number of electric devices; gamma raytRepresents the electricity price at time t; p is a radical ofi,tIndicating the probability of use of the powered device i at time t.
It will be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is merely illustrated, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the power specific customer electricity price responsivity determining apparatus is divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 4 is a schematic block diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 4, the terminal device 40 of this embodiment includes: one or more processors 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processors 401. The processor 401 implements the steps in each of the above-described embodiments of the power-dedicated-customer electricity price responsiveness determining method, such as steps S101 to S103 shown in fig. 1, when executing the computer program 403. Alternatively, the processor 401, when executing the computer program 403, implements the functions of the modules/units in the above-described power specific customer electricity price responsiveness determination apparatus embodiment, for example, the functions of the modules 301 to 303 shown in fig. 3.
Illustratively, the computer program 403 may be partitioned into one or more modules/units that are stored in the memory 402 and executed by the processor 401 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program 403 in the terminal device 40. For example, the computer program 403 may be divided into an acquisition module, a load decomposition module, and an electricity price responsiveness determination module, each module having the following specific functions:
the acquisition module is used for acquiring historical electricity load data and electricity utilization equipment of the power special transformer client and acquiring electricity price data;
the load decomposition module is used for carrying out load decomposition on the power special transformer client based on historical power load data and power utilization equipment to obtain the use distribution information of each power utilization equipment of the power special transformer client;
and the electricity price responsivity determining module is used for determining the electricity price responsivity of the electricity special transformer customer according to the use distribution information and the electricity price data of each electric device of the electricity special transformer customer.
Other modules or units can refer to the description of the embodiment shown in fig. 3, and are not described again here.
The terminal device 40 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device 40 includes, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that fig. 4 is only one example of a terminal device 40, and does not constitute a limitation to the terminal device 40, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal device 40 may further include an input device, an output device, a network access device, a bus, etc.
The Processor 401 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 402 may be an internal storage unit of the terminal device 40, such as a hard disk or a memory of the terminal device 40. The memory 402 may also be an external storage device of the terminal device 40, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 40. Further, the memory 402 may also include both an internal storage unit of the terminal device 40 and an external storage device. The memory 402 is used for storing the computer program 403 and other programs and data required by the terminal device 40. The memory 402 may also be used to temporarily store data that has been output or is to be output.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed power specific customer price responsiveness determining apparatus and method may be implemented in other ways. For example, the above-described power specific customer price responsiveness determination device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical functional division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A power special transformer customer electricity price responsivity determining method is characterized by comprising the following steps:
acquiring historical electricity load data and electricity utilization equipment of a power special transformer client, and acquiring electricity price data;
performing load decomposition on the power special transformer customer based on the historical power load data and the power utilization equipment to obtain the use distribution information of each power utilization equipment of the power special transformer customer;
and determining the electricity price responsivity of the electricity special change customer according to the use distribution information of each electricity utilization device of the electricity special change customer and the electricity price data.
2. The method for determining the power rate responsiveness of the power specific transformation client according to claim 1, wherein the load splitting of the power specific transformation client based on the historical power load data and the power consumption equipment to obtain the usage distribution information of each power consumption equipment of the power specific transformation client comprises:
modeling the power load of the power special transformer customer based on a hidden Markov model to obtain an initial load model;
training the initial load model according to the historical power load data based on an EM algorithm to obtain a final load model;
based on a Viterbi algorithm, carrying out load decomposition according to the final load model to obtain a power consumption state sequence set;
and determining the use distribution information of each piece of electric equipment according to the electric utilization state sequence set.
3. The power rate responsiveness determining method according to claim 1 or 2, wherein the determining of the power rate responsiveness of the power specific customer based on the usage distribution information of the respective power consumers of the power specific customer and the power rate data includes:
calculating the electricity price responsivity of each electric device of the power special transformer customer according to the use distribution information of each electric device of the power special transformer customer and the electricity price data;
and determining the average value of the power rate responsivity of each electric device of the power special transformer customer as the power rate responsivity of the power special transformer customer.
4. The power rate responsiveness determining method according to claim 3, wherein the calculating of the power rate responsiveness of each of the power consumption appliances of the power specific transformation client based on the usage distribution information of each of the power consumption appliances of the power specific transformation client and the power rate data includes:
aiming at each electric device of the power special transformer customer, calculating the value of each parameter of the electric device according to the use distribution information of the electric device and the electricity price data, and carrying out standardization processing on the value of each parameter of the electric device by adopting a range conversion method to obtain the standard value of each parameter of the electric device;
determining the weight of each parameter of the electric equipment based on an entropy weight method;
and determining the electricity price responsivity of the electric equipment according to the standard value of each parameter of the electric equipment and the weight of each parameter of the electric equipment.
5. The power specific customer price responsiveness according to claim 4, wherein the parameters include a variance of the power usage distribution, a gray correlation of the power usage distribution with a negative value of the power price, and a weighted average of the power prices at the same time with respect to the probability of use of the power consumer.
6. The power-specific customer electricity price responsiveness determination method according to claim 5, wherein the usage distribution information of each of the electric devices of the power-specific customer includes a usage probability of each of the electric devices of the power-specific customer at each time;
the calculation formula of the grey correlation degree of the power consumption power distribution and the electricity price negative value is
The calculation formula of the weighted average of the electricity prices at the same time and the use probability of the electric equipment is
Wherein T represents the total number of sampling instants within a day; m represents the number of electric devices; gamma raytRepresents the electricity price at time t; p is a radical ofi,tIndicating the probability of use of the powered device i at time t.
7. An electricity rate responsivity determining apparatus for a power specific customer, comprising:
the acquisition module is used for acquiring historical electricity load data and electricity utilization equipment of the power special transformer client and acquiring electricity price data;
the load decomposition module is used for carrying out load decomposition on the power special transformer customer based on the historical power load data and the power utilization equipment to obtain the use distribution information of each power utilization equipment of the power special transformer customer;
and the electricity price responsivity determining module is used for determining the electricity price responsivity of the electricity special transformer customer according to the use distribution information of each electric device of the electricity special transformer customer and the electricity price data.
8. The power specific customer price responsivity determining apparatus of claim 7 wherein the load resolution module is specifically configured to:
modeling the power load of the power special transformer customer based on a hidden Markov model to obtain an initial load model;
training the initial load model according to the historical power load data based on an EM algorithm to obtain a final load model;
based on a Viterbi algorithm, carrying out load decomposition according to the final load model to obtain a power consumption state sequence set;
and determining the use distribution information of each piece of electric equipment according to the electric utilization state sequence set.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the power specific customer electricity price responsiveness determining method according to any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by one or more processors, implements the steps of the power special change customer electricity price responsiveness determination method according to any one of claims 1 to 6.
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