CN113469448A - Time-of-use electricity price time interval division optimization method, system, terminal device and medium - Google Patents
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Abstract
The invention discloses a time-sharing electricity price time interval division optimization method, a time-sharing electricity price time interval division optimization system, terminal equipment and a medium, wherein the method comprises the following steps: acquiring daily load data of a user, and determining peak membership and valley membership of each time point by using a fuzzy half-gradient membership function; determining an initial time interval division scheme according to a fuzzy clustering algorithm, the peak membership degree and the valley membership degree; and correcting the initial time interval division scheme by using the user responsiveness attribute index to obtain a target time interval division scheme. The time-of-use electricity price time interval division optimization method provided by the invention optimizes and adjusts the initial time interval division scheme by constructing the user responsiveness attribute index, and provides a basis for the subsequent pricing model optimization. The method can not only enhance the energy efficiency of peak clipping and valley filling, but also relieve the problem of unbalanced power supply and demand, thereby improving the social and economic benefits.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a time-sharing electricity price time interval division optimization method, a time-sharing electricity price time interval division optimization system, terminal equipment and a medium.
Background
The time-of-use electricity price refers to that relevant government departments and power supply enterprises combine with the operation condition of a power grid, 24 hours a day are divided into a plurality of time intervals, and the price is set according to the average marginal cost of system operation in each time interval. The reasonable time-of-use electricity price can effectively stimulate the user to optimize the electricity utilization mode of the user, participate in the load adjustment of the power grid, and play a role in peak clipping and valley filling. The time-of-use electricity price is the most common and widely applied measure in the demand side management economic measures.
However, with the continuous improvement of the power metering system and the communication system, the intelligent device continuously enters the daily production and life of people, and power users have further knowledge on participating in demand side management and reducing production and life costs, so that the existing time-of-use electricity price scheme cannot meet the user demands in both time interval division scheme and peak-to-valley electricity price aspect, for example, the peak clipping and valley filling function is weaker, and the power supply and demand still have a larger imbalance problem, thereby further affecting the economic benefit.
In summary, a time-sharing electricity price time interval division optimization method is needed to enhance peak load shifting energy efficiency and alleviate the problem of unbalanced power supply and demand.
Disclosure of Invention
The invention aims to provide a time-sharing electricity price time interval division optimization method, a time-sharing electricity price time interval division optimization system, terminal equipment and a time-sharing electricity price time interval division optimization medium, and aims to solve the technical problems that peak clipping and valley filling energy efficiency is low and power supply and demand imbalance is serious due to unreasonable time interval division in the existing time-sharing electricity price scheme.
In order to overcome the defects in the prior art, the invention provides a time-sharing electricity price time interval division optimization method, which comprises the following steps:
acquiring daily load data of a user, and determining peak membership and valley membership of each time point by using a fuzzy half-gradient membership function;
determining an initial time interval division scheme according to a fuzzy clustering algorithm, the peak membership degree and the valley membership degree;
and correcting the initial time interval division scheme by using the user responsiveness attribute index to obtain a target time interval division scheme.
Further, the determining an initial time interval division scheme according to the fuzzy clustering algorithm, the peak membership and the valley membership comprises:
establishing a time point attribute set according to the peak membership degree and the valley membership degree;
constructing an original attribute matrix by using the time point attribute set;
carrying out standardization processing on the original attribute matrix;
establishing a fuzzy similar matrix according to the standardized processing result;
and performing dynamic clustering according to the fuzzy similarity matrix, and determining an initial time period division scheme according to a dynamic clustering result.
Further, the modifying the initial period division scheme by using the user responsiveness attribute index includes:
calculating the responsibility attribute value of each time point by using the user responsibility attribute index;
and when the responsibility attribute value of a certain time point is a non-positive number, repeatedly executing the step of establishing a time point attribute set according to the peak membership and the valley membership until the responsibility attribute value of the time point is a positive number.
Further, after the initial time interval division scheme is modified to obtain a target time interval division scheme, the method further includes:
forming an index set by using the attribute indexes of peak membership, valley membership and responsivity;
judging whether the time interval attribute of each time point in the target time interval division scheme is consistent with the time interval attribute of each time point in the actual time-of-use electricity price scheme or not according to the index set;
if so, checking the rationality of the target time interval division scheme to pass;
if not, dynamically adjusting the inconsistent time points.
Further, the initial time division scheme is as follows:
wherein tau is a peak-to-valley membership characteristic threshold, TpIs a peak time point set, TlIs a set of low trough time points, TfIn the form of a set of flat-segment time points,
is the peak membership of the time point,
is the trough membership of the time point.
The invention also provides a time-sharing electricity price time interval division optimizing system, which comprises:
the membership calculation unit is used for acquiring daily load data of the user and determining peak membership and valley membership of each time point by using a fuzzy half-gradient membership function;
the initial scheme determining unit is used for determining an initial time interval division scheme according to a fuzzy clustering algorithm, the peak membership degree and the valley membership degree;
and the correcting unit is used for correcting the initial time interval division scheme by utilizing the user responsiveness attribute index to obtain a target time interval division scheme.
Further, the initial scheme determining unit is further configured to:
establishing a time point attribute set according to the peak membership degree and the valley membership degree;
constructing an original attribute matrix by using the time point attribute set;
carrying out standardization processing on the original attribute matrix;
establishing a fuzzy similar matrix according to the standardized processing result;
and performing dynamic clustering according to the fuzzy similarity matrix, and determining an initial time period division scheme according to a dynamic clustering result.
Further, the correction unit is further configured to:
calculating the responsibility attribute value of each time point by using the user responsibility attribute index;
and when the responsibility attribute value of a certain time point is a non-positive number, repeatedly executing the step of establishing a time point attribute set according to the peak membership and the valley membership until the responsibility attribute value of the time point is a positive number.
The present invention also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the time of use electricity price time division optimization method as described in any one of the above when executing the computer program.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which is executed by a processor to implement the time of use electricity price period division optimizing method as described in any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses a time-sharing electricity price time interval division optimization method, which comprises the following steps: acquiring daily load data of a user, and determining peak membership and valley membership of each time point by using a fuzzy half-gradient membership function; determining an initial time interval division scheme according to a fuzzy clustering algorithm, the peak membership degree and the valley membership degree; and correcting the initial time interval division scheme by using the user responsiveness attribute index to obtain a target time interval division scheme.
The time-of-use electricity price time interval division optimization method provided by the invention optimizes and adjusts the initial time interval division scheme by constructing the user responsiveness attribute index, and provides a basis for the subsequent pricing model optimization. The method can not only enhance the energy efficiency of peak clipping and valley filling, but also relieve the problem of unbalanced power supply and demand, thereby improving the social and economic benefits.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments 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 that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a time-of-use electricity price time interval division optimization method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a time-of-use electricity price time division optimization method according to another embodiment of the present invention;
fig. 3 is a schematic structural diagram of a time-of-use electricity price period optimization system according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not used as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
In a first aspect:
referring to fig. 1, an embodiment of the present invention provides a time-sharing electricity price time interval division optimizing method, including:
and S10, acquiring daily load data of the user, and determining the peak membership degree and the valley membership degree of each time point by using a fuzzy half-gradient membership function.
And S20, determining an initial time interval division scheme according to the fuzzy clustering algorithm, the peak membership degree and the valley membership degree.
And S30, correcting the initial time interval division scheme by using the user responsiveness attribute index to obtain a target time interval division scheme.
It should be noted that the peak-valley time-of-use electricity price of the three-segment system is popularized in most areas of China at present, general industrial and commercial users and large-scale industrial users mostly adopt the three-segment system, and even in some areas, a four-segment electricity price scheme is executed at the peak of electricity consumption in summer and winter, namely, the peak electricity price is implemented. In addition, in some cities which develop faster along the coast, two-stage system electricity price is adopted for the electricity consumption of urban residents. By gradually popularizing the time-of-use electricity price measures, good effects are achieved in all places, the electricity consumption in the peak time is obviously reduced, and the load rate of the system is gradually improved; the switching-off and power-limiting times are reduced, and the power supply reliability is correspondingly improved. The time interval division and pricing mechanism is the part which is one of the time-of-use electricity price schemes and is not available when the time-of-use electricity price scheme is formulated, the scheme for formulating reasonable time interval division is the basis for effectively implementing the time-of-use electricity price scheme, and the peak-valley time interval division capability is also necessary to establish a dynamic adjustment mechanism along with the continuous enhancement of the interaction capability of the supply and demand parties. Therefore, in this embodiment, the response of the user at each time point is evaluated based on the initial time division scheme, so as to dynamically adjust the time division result.
The optimization method in this embodiment mainly divides three stages, that is, the optimization method is composed of three parts, namely, an initial peak-valley period division, a user responsiveness attribute index, and a period division correction based on user responsiveness, as shown in fig. 2.
Specifically, in step S10, typical daily load data of a certain class of users is first obtained, and then peak membership and valley membership at each time point are determined by using a fuzzy half-gradient membership function.
In one embodiment, the set of time points on the load curve is a classification object, i.e., S ═ { t ═ t {1,t2,…,t24Get the peak membership degree mu therefrompDegree of membership of Hegu mui。
Further, in step S20, an initial time interval division scheme is determined according to the fuzzy clustering algorithm, the peak membership and the valley membership.
In one embodiment, the main method in step S20 is to construct a time point attribute set by using a fuzzy clustering scheme, determine an original attribute matrix X, standardize the original attribute matrix to establish a fuzzy similarity matrix R, perform dynamic clustering according to the fuzzy similarity matrix, determine an optimal threshold of peak and valley characteristics, and obtain a basic peak and valley period partitioning scheme. The main flow of step S20 is shown in fig. 2.
Specifically, step S20 includes the following sub-steps:
and 2.1) establishing a time point attribute set according to the peak membership degree and the valley membership degree.
In this step, each time point attribute index is selected, and a time period attribute set is formed by the peak and valley membership degrees of the time points, that is:
xi＝(xi1,xi2)
wherein, i is 1,2, …,24,
2.2) constructing an original attribute matrix by using the time point attribute set.
Based on the time point attribute set in step 2.1), obtaining an original data matrix as follows:
2.3) carrying out standardization processing on the original attribute matrix.
Specifically, normalizing X yields:
wherein i is 1,2, …, 24; k is 1, 2; mean value of
Standard deviation of
2.4) establishing a fuzzy similarity matrix according to the standardized processing result.
Specifically, the similarity coefficient at each time point is calculated, and a fuzzy similarity matrix R is established.
Wherein i is 1,2, …, 24; c is equal to [0,1]]Is a suitable parameter selected such that rij∈[0，1]，
Denotes xiAnd xjThe distance of (c).
And 2.5) carrying out dynamic clustering according to the fuzzy similarity matrix, and determining an initial time interval division scheme according to a dynamic clustering result.
2.5.1) determining the lambda-cut matrix R of the fuzzy similarity matrix from the fuzzy propagation closureλ. Successively obtaining R (R) according to a quadratic methodij)24×24The transitive closure of (a), (b), namely:
t(R)＝(tij)24×24
determining a lambda-cut matrix R of t (R)λThe method comprises the following steps:
Rλ＝(λγij)24×24
wherein i, j is 1,2, …, 24; lambda belongs to [0,1 ].
Further, the optimum lambda value is determined based on the magnitude of the F statistic. In practical application, however, in addition to the size of the statistic, the scientificity and reasonableness of the time slot design are also considered.
2.5.2) when the clustering number takes 3, a peak time point set T can be obtainedpFlat segment time point set TfAnd low valley time point set TlThus, an initial time interval division scheme is formed, and the calculation formula is as follows:
wherein τ is a peak-to-valley membership characteristic threshold, and the membership characteristic threshold is set in this embodimentThe content was 70%. If the peak membership of a time point is greater than this threshold, the peak characteristics characterizing the time point are significant, and therefore the time point should be shifted into the peak period TpAnd valley period TlThe same process is carried out; for some time points, the degree of membership of the peak and the trough of the time points is less than the characteristic threshold, and the time points can be classified into a flat time point set Tf. And finally forming an initial time interval division scheme based on the peak and valley membership degree attributes.
Is the peak membership of the time point,
is the trough membership of the time point.
And S30, correcting the initial time interval division scheme by using the user responsiveness attribute index to obtain a target time interval division scheme.
Specifically, step S30 includes the following substeps:
and 3.1) calculating the responsibility attribute value of each time point by using the user responsibility attribute index.
3.2) when the responsibility attribute value of a certain time point is a non-positive number, repeatedly executing the step of establishing a time point attribute set according to the peak membership and the valley membership until the responsibility attribute value of the time point is a positive number.
It should be noted that, as shown in fig. 2, when the responsivity attribute value at a certain point is a non-positive number, the period is determined again according to the membership. The method is to return to the step S20 to obtain a new time interval division scheme, and obtain a target time interval division scheme when the responsiveness attribute value of the time point is positive.
In one embodiment, after step S30, the method further includes:
4.1) forming an index set by utilizing the attribute indexes of peak membership, valley membership and responsivity.
4.2) judging whether the time interval attribute of each time point in the target time interval division scheme is consistent with the time interval attribute of each time point in the actual time-of-use electricity price scheme or not according to the index set;
if so, checking the rationality of the target time interval division scheme to pass;
if not, dynamically adjusting the inconsistent time points.
According to the time-of-use electricity price time interval division optimization method provided by the embodiment of the invention, the initial time interval division scheme is optimized and adjusted by constructing the user responsiveness attribute index, so that a basis is provided for the optimization of a subsequent pricing model. The method can not only enhance the energy efficiency of peak clipping and valley filling, but also relieve the problem of unbalanced power supply and demand, thereby improving the social and economic benefits.
In a second aspect:
referring to fig. 3, an embodiment of the present invention further provides a time-sharing electricity price time interval division optimizing system, including:
the membership degree calculating unit 01 is used for acquiring daily load data of a user and determining peak membership degrees and valley membership degrees of each time point by using a fuzzy half-gradient membership degree function;
an initial scheme determining unit 02, configured to determine an initial time interval division scheme according to a fuzzy clustering algorithm, the peak membership degree, and the valley membership degree;
and the correcting unit 03 is configured to correct the initial time interval division scheme by using the user responsiveness attribute index, so as to obtain a target time interval division scheme.
In an embodiment, the initial scheme determining unit 02 is further configured to:
establishing a time point attribute set according to the peak membership degree and the valley membership degree;
constructing an original attribute matrix by using the time point attribute set;
carrying out standardization processing on the original attribute matrix;
establishing a fuzzy similar matrix according to the standardized processing result;
and performing dynamic clustering according to the fuzzy similarity matrix, and determining an initial time period division scheme according to a dynamic clustering result.
In an embodiment, the modifying unit 03 is further configured to:
calculating the responsibility attribute value of each time point by using the user responsibility attribute index;
and when the responsibility attribute value of a certain time point is a non-positive number, repeatedly executing the step of establishing a time point attribute set according to the peak membership and the valley membership until the responsibility attribute value of the time point is a positive number.
The time-of-use electricity price time interval division optimization system provided by the embodiment of the invention is used for executing the time-of-use electricity price time interval division optimization method in the first aspect. The method optimizes and adjusts the initial period division scheme by constructing the user responsiveness attribute index, and provides a basis for the optimization of a subsequent pricing model. The method can not only enhance the energy efficiency of peak clipping and valley filling, but also relieve the problem of unbalanced power supply and demand, thereby improving the social and economic benefits.
Third aspect of the invention
Referring to fig. 4, an embodiment of the present invention further provides a terminal device, where the terminal device includes:
a processor, a memory, and a bus;
the bus is used for connecting the processor and the memory;
the memory is used for storing operation instructions;
the processor is configured to call the operation instruction, and the executable instruction enables the processor to execute an operation corresponding to the time-of-use electricity price time interval division optimization method shown in the first aspect of the application.
In an alternative embodiment, there is provided a terminal device, as shown in fig. 4, the terminal device shown in fig. 4 includes: a processor 001 and a memory 003. Where processor 001 is coupled to memory 003, such as by bus 002. Optionally, the terminal device may also include a transceiver 004. It should be noted that the transceiver 004 is not limited to one in practical application, and the structure of the terminal device does not constitute a limitation to the embodiments of the present application.
The processor 001 may be a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 001 may also be a combination that performs computing functions, including for example, one or more microprocessors, a combination of DSPs and microprocessors, and the like.
The memory 003 can be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an EEPROM, a CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
The memory 003 is used for storing application program codes for performing the present solution and is controlled in execution by the processor 001. Processor 001 is configured to execute application code stored in memory 003 to implement any of the method embodiments described above.
Wherein, the terminal device includes but is not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like.
Yet another embodiment of the present application provides a computer-readable storage medium having stored thereon a computer program, which, when run on a computer, causes the computer to perform the respective ones of the aforementioned method embodiments.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A time-sharing electricity price time interval division optimization method is characterized by comprising the following steps:
acquiring daily load data of a user, and determining peak membership and valley membership of each time point by using a fuzzy half-gradient membership function;
determining an initial time interval division scheme according to a fuzzy clustering algorithm, the peak membership degree and the valley membership degree;
and correcting the initial time interval division scheme by using the user responsiveness attribute index to obtain a target time interval division scheme.
2. The time-of-use electricity price time interval division optimization method according to claim 1, wherein the determining an initial time interval division scheme according to the fuzzy clustering algorithm, the peak membership degree and the valley membership degree comprises:
establishing a time point attribute set according to the peak membership degree and the valley membership degree;
constructing an original attribute matrix by using the time point attribute set;
carrying out standardization processing on the original attribute matrix;
establishing a fuzzy similar matrix according to the standardized processing result;
and performing dynamic clustering according to the fuzzy similarity matrix, and determining an initial time period division scheme according to a dynamic clustering result.
3. The time-of-use electricity price time division optimization method according to claim 2, wherein the modifying the initial time division scheme by using the user responsiveness property index comprises:
calculating the responsibility attribute value of each time point by using the user responsibility attribute index;
and when the responsibility attribute value of a certain time point is a non-positive number, repeatedly executing the step of establishing a time point attribute set according to the peak membership and the valley membership until the responsibility attribute value of the time point is a positive number.
4. The time-of-use electricity price time-of-day partition optimization method according to claim 3, wherein after the initial time-of-day partition scheme is modified to obtain a target time-of-day partition scheme, the method further comprises:
forming an index set by using the attribute indexes of peak membership, valley membership and responsivity;
judging whether the time interval attribute of each time point in the target time interval division scheme is consistent with the time interval attribute of each time point in the actual time-of-use electricity price scheme or not according to the index set;
if so, checking the rationality of the target time interval division scheme to pass;
if not, dynamically adjusting the inconsistent time points.
5. The time-of-use electricity price time division optimization method according to any one of claims 1 to 4, wherein the initial time division scheme is:
wherein tau is a peak-to-valley membership characteristic threshold, TpIs a peak time point set, TlIs a set of low trough time points, TfIn the form of a set of flat-segment time points,
is the peak membership of the time point,
is the trough membership of the time point.
6. A time-of-use electricity price time division optimization system, comprising:
the membership calculation unit is used for acquiring daily load data of the user and determining peak membership and valley membership of each time point by using a fuzzy half-gradient membership function;
the initial scheme determining unit is used for determining an initial time interval division scheme according to a fuzzy clustering algorithm, the peak membership degree and the valley membership degree;
and the correcting unit is used for correcting the initial time interval division scheme by utilizing the user responsiveness attribute index to obtain a target time interval division scheme.
7. The time-of-use electricity price time division optimization system according to claim 6, wherein the initial plan determination unit is further configured to:
establishing a time point attribute set according to the peak membership degree and the valley membership degree;
constructing an original attribute matrix by using the time point attribute set;
carrying out standardization processing on the original attribute matrix;
establishing a fuzzy similar matrix according to the standardized processing result;
and performing dynamic clustering according to the fuzzy similarity matrix, and determining an initial time period division scheme according to a dynamic clustering result.
8. The time-of-use electricity price time division optimization system according to claim 7, wherein the modification unit is further configured to:
calculating the responsibility attribute value of each time point by using the user responsibility attribute index;
and when the responsibility attribute value of a certain time point is a non-positive number, repeatedly executing the step of establishing a time point attribute set according to the peak membership and the valley membership until the responsibility attribute value of the time point is a positive number.
9. A terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the time of use electricity price time division optimization method of any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium on which a computer program is stored, the computer program being executed by a processor to implement the time-of-use electricity price period division optimizing method according to any one of claims 1 to 5.
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