CN116632833A

CN116632833A - Method and device for determining peak-to-valley period of power system

Info

Publication number: CN116632833A
Application number: CN202310645174.2A
Authority: CN
Inventors: 程兰芬; 杜育斌; 周保荣; 李燕平; 禤培正; 刘结; 朱继松; 敖健永; 苏祥瑞; 吕贤利; 唐翀; 余玉晗; 卓华硕; 黄秀秀
Original assignee: CSG Electric Power Research Institute; Yangjiang Power Supply Bureau of Guangdong Power Grid Co Ltd
Current assignee: CSG Electric Power Research Institute; Yangjiang Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date: 2023-05-31
Filing date: 2023-05-31
Publication date: 2023-08-22

Abstract

The application provides a method and a device for determining peak-to-valley time periods of a power system, wherein the method comprises the following steps: acquiring historical electricity consumption net load quantity, and determining a target time period according to the historical electricity consumption net load quantity; acquiring the electricity consumption net load of each first sub-time period of the target time period, and determining the first target sub-time period according to the electricity consumption net load of each first sub-time period of the target time period; and acquiring the electricity consumption payload quantity of each second sub-time period of the first sub-time period, and determining a second target sub-time period according to the electricity consumption payload quantity of each second sub-time period of the first sub-time period. The problem that the supply and demand conditions of an actual novel power system cannot be truly reflected in the prior art is solved.

Description

Method and device for determining peak-to-valley period of power system

Technical Field

The present application relates to a novel power system, and more particularly, to a method of determining peak-to-valley period of a power system, a determination apparatus, a computer-readable storage medium, and an electronic apparatus.

Background

In the context of large-scale grid connection of new energy, in order to support the construction of a novel power system with high-proportion new energy, the problem of difficult matching between the power supply uncertainty and the fluctuation of user demands, which is aggravated by the market entry of new energy, is needed to be solved. The price lever plays a role in peak clipping and valley filling, and can effectively solve the problem of supply and demand balance. The time-sharing electricity price is commonly carried out everywhere at the present stage, and the peak flat valley time-sharing electricity price of partial test points. At present, the related research for deep valley period is relatively less, most researchers are limited to the research of peak period and electricity price, and consumers are forced to transfer load demands through higher electricity price, which shows the value of electric quantity in the market of supply and demand, but is unfavorable for the consumption of new energy in the market of supply and demand and violates the market mechanism.

However, in the prior art, the real supply and demand conditions are difficult to reflect by dividing the time period directly according to the load curve, and the problems of uncertainty of the power generation side and the like of the duck curve brought by new energy into the market cannot be well solved.

Therefore, a method is needed to solve the problem that the supply and demand conditions of the actual novel power system cannot be truly reflected in the prior art.

Disclosure of Invention

The application aims to provide a method and a device for determining peak-to-valley time periods of a power system, a computer readable storage medium and an electronic device, so as to at least solve the problem that the supply and demand conditions of an actual novel power system cannot be truly reflected in the prior art.

According to an aspect of the present application, there is provided a method for determining peak-to-valley period of a power system, including: acquiring historical electricity consumption payload quantity, and determining a target time period according to the historical electricity consumption payload quantity, wherein the historical electricity consumption payload quantity comprises electricity consumption payload quantity of each historical time period in a plurality of historical time periods, the target time period comprises deep time periods and/or peak time periods, the deep time periods are the first n time periods in ascending order of the electricity consumption payload quantity in the historical electricity consumption payload quantity, and the peak time periods are the first n time periods in descending order of the electricity consumption payload quantity in the historical electricity consumption payload quantity; acquiring the electricity consumption payload quantity of each first sub-period of the target period, and determining a first target sub-period according to the electricity consumption payload quantity of each first sub-period of the target period, wherein the first target sub-period comprises a first deep valley period and/or a first peak sub-period, the first target sub-period comprises the first deep valley period when the target period comprises the deep valley period, and the first target sub-period comprises the first peak sub-period when the target period comprises the peak period; obtaining the electric payload amount of each second sub-period of the first sub-period, and determining a second target sub-period according to the electric payload amount of each second sub-period of the first sub-period, wherein the first target sub-period comprises the second target sub-period, the second target sub-period comprises a second deep valley period and/or a second peak sub-period, the second target sub-period comprises the second deep valley period when the first target sub-period comprises the first deep valley period, the second target sub-period comprises the second peak sub-period when the first target sub-period comprises the first peak sub-period, and the second target sub-period is used for adjusting the electric price of a novel electric power system.

Optionally, obtaining the historical electricity usage payload comprises: acquiring the power generation load capacity of the novel power system, the external power generation load capacity of the novel power system, the power generation load capacity of the wind power generation of the novel power system, the power generation load capacity of the photovoltaic power generation of the novel power system and the power generation load capacity of the novel power system; according to the formula: l (L) _{NL_t} ＝L _{CL_t} -P _{ip_t} -P _{wi_t} -P _{S_t} +P _{OP_t} Determining the historical electricity payload quantity, wherein L _{NL_t} For said historical electric payload, L _{CL_t} For the power generation load of the novel power system, P _{ip_t} For the external electric load quantity of the novel electric power system, P _{wi_t} Generating power for wind power generation of the novel power systemLoad amount, P _{S_t} Power generation load amount, P, for photovoltaic power generation of the novel power system _{OP_t} And generating load quantity for the output of the novel power system.

Optionally, determining the target time period according to the historical electricity payload amount includes: according to the formula: m is M _p ＝argsort(M ₁ ,M ₂ ,...,M ₁₂ ) Determining the target time period, wherein M _p For the target time period, argsort () is used to sort the elements in the array from small to large or from large to small, M _i For the historical electrical payload, i is any integer value from 1 to 12.

Optionally, determining the first deep millet period according to the amount of electric net load of each first sub-period of the target period includes: determining the minimum value of the electric net load in a plurality of first sub-time periods as the minimum load; determining that the target time period satisfies a condition:is said first deep millet period, wherein +.f.>For the amount of payload of electricity at a first moment of said first sub-period of said target period of time,/for a first time period of said target period of time>For the amount of payload used at a second instant of the first sub-period of the target period,an amount of payload of electricity x, which is a third time instant of the first sub-period of the target period of time _min Gamma, the minimum load ₁ The first time is a time before the second time, and the third time is a time next to the second time, which is a first predetermined coefficient.

Optionally according to the target timeDetermining the first peak sub-period of time includes: determining the maximum value of the electric net load in a plurality of first sub-time periods as the maximum load; determining that the target time period satisfies a condition: Is the first peak sub-period, wherein +_f->For the amount of payload of electricity at a fourth time instant of said first sub-period of said target period of time,/for a fourth time instant of said first sub-period of said target period of time>For the amount of payload of electricity at the fifth moment of said first sub-period of said target period of time,/for the first sub-period of time>An amount of electric net load, x, at a sixth time instant of the first sub-period of the target period _max For the maximum load amount, gamma ₂ The fourth time is a time preceding the fifth time, and the sixth time is a time next to the fifth time, which is a second predetermined coefficient.

Optionally, determining a second target sub-period according to the amount of electricity payload of each second sub-period of the first sub-period includes: acquiring a matrix composed of the second sub-time periods of the first sub-time periodWherein S is ⁿ For the matrix>The second sub-period being an mth of the first sub-period; processing the matrix by adopting a differential evolution algorithm to obtain an initial clustering center of the matrix; according to the initial aggregationAnd the class center performs clustering processing on the matrix by adopting a fuzzy K-means algorithm to obtain the second target sub-time period.

Optionally, processing the matrix by using a differential evolution algorithm to obtain an initial cluster center of the matrix, including: generating, namely generating a corresponding initial population according to each column element of the matrix, wherein the initial population comprises a plurality of initial individuals; a mutation step of performing mutation operation on the initial population according to any initial individual and scaling factors in the initial population to obtain a plurality of mutated individuals; a crossing step, according to the variant individuals and the initial individuals, carrying out crossing operation on the variant individuals to obtain crossed individuals; determining, namely determining the maximum value of the fitness function value of the crossed individual and the fitness function value of the initial individual as a target value; and repeating the mutation step, the crossing step and the determining step at least once under the condition that the target value is larger than a first threshold value until the target value is smaller than or equal to the first threshold value, and determining that the individual corresponding to the target value is the initial clustering center.

Optionally, clustering the matrix by adopting a fuzzy K-means algorithm according to the initial clustering center to obtain the second target sub-time period, including: a calculation step according to Calculating a criterion function of a membership matrix of the initial cluster center, wherein J _m (U, C) is a criterion function value of the membership matrix,/and>d (S) is a fuzzy matrix of the second sub-period of the first sub-period and the initial cluster center _j ,C _i ) M is the number of the second sub-time periods of the first sub-time period and K is the number of the second target sub-time periods; in the condition that the criterion function value is larger than or equal toAnd under the condition of a second threshold value, updating the initial clustering center and repeating the calculating step at least once until the criterion function value is smaller than the second threshold value, and determining a data cluster corresponding to the clustering center corresponding to the criterion function as the second target sub-time period.

According to another aspect of the present application, there is provided a peak-to-valley period determination apparatus of an electric power system, including: a first determining unit, configured to obtain a historical electricity payload amount, and determine a target time period according to the historical electricity payload amount, where the historical electricity payload amount includes an electricity payload amount of each of a plurality of historical time periods, the target time period includes a deep valley time period and/or a peak time period, the deep valley time period is a first n time periods in ascending order of the electricity payload amounts in the historical electricity payload amounts, and the peak time period is a first n time periods in descending order of the electricity payload amounts in the historical electricity payload amounts; a second determining unit, configured to obtain an electricity payload amount of each first sub-period of the target period, and determine a first target sub-period according to the electricity payload amount of each first sub-period of the target period, where the first target sub-period includes a first deep valley period and/or a first peak sub-period, where the first target sub-period includes the first deep valley period if the target period includes the deep valley period, and where the first target sub-period includes the first peak sub-period if the target period includes the peak period; a third determining unit, configured to obtain an electricity consumption payload amount of each second sub-period of the first sub-period, and perform clustering processing on the electricity consumption payload amount of each second sub-period of the first sub-period, and determine a second target sub-period, where the first target sub-period includes the second target sub-period, the second target sub-period includes a second deep valley period and/or a second peak sub-period, where the second target sub-period includes the second deep valley period when the first target sub-period includes the first deep valley period, and where the second target sub-period includes the second peak sub-period when the first target sub-period includes the first peak sub-period, the second target sub-period is used to adjust an electricity price of a novel electric power system.

According to still another aspect of the present application, there is provided a computer readable storage medium including a stored program, wherein the program when run controls a device in which the computer readable storage medium is located to perform any one of the methods.

According to a further aspect of the application there is provided an electronic device comprising a memory having a computer program stored therein and a processor arranged to perform any one of the methods by means of the computer program.

By applying the technical scheme of the application, firstly, the historical electricity consumption payload quantity is obtained, and a target time period is determined according to the historical electricity consumption payload quantity; acquiring the electricity consumption net load of each first sub-time period of the target time period, and determining the first target sub-time period according to the electricity consumption net load of each first sub-time period of the target time period; and finally, acquiring the electricity consumption payload quantity of each second sub-time period of the first sub-time period, and determining a second target sub-time period according to the electricity consumption payload quantity of each second sub-time period of the first sub-time period. The electricity utilization net load of the novel power system is selected, peak Gu Yue and peak valley days are further selected, and then a plurality of peak valley time periods of the peak valley days are processed to determine the final peak valley time period, so that the actual supply and demand conditions can be reflected, and further the peak valley time period is determined. The problem that the supply and demand conditions of an actual novel power system cannot be truly reflected in the prior art is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

fig. 1 is a block diagram showing a hardware configuration of a mobile terminal performing a method of determining peak-to-valley period of a power system according to an embodiment of the present application;

fig. 2 is a flow chart of a method for determining peak-to-valley period of an electric power system according to an embodiment of the present application;

fig. 3 is a specific flow diagram of a method for determining peak-to-valley periods of an electric power system according to an embodiment of the present application;

fig. 4 shows a block diagram of a determination apparatus of peak-to-valley period of a power system according to an embodiment of the present application.

Wherein the above figures include the following reference numerals:

102. a processor; 104. a memory; 106. a transmission device; 108. and an input/output device.

Detailed Description

It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the application herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

As described in the background art, in order to solve the problem that the supply and demand conditions of an actual novel power system cannot be truly reflected in the prior art, embodiments of the present application provide a method for determining peak-to-valley periods of a power system, a determining device, a computer-readable storage medium, and an electronic device.

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.

The method embodiments provided in the embodiments of the present application may be performed in a mobile terminal, a computer terminal or similar computing device. Taking the mobile terminal as an example, fig. 1 is a block diagram of a hardware structure of the mobile terminal of a method for determining peak-to-valley periods of a power system according to an embodiment of the present application. As shown in fig. 1, a mobile terminal may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and not limiting of the structure of the mobile terminal described above. For example, the mobile terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.

The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to a method for determining a peak-to-valley period of a power system in an embodiment of the present invention, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, to implement the above-described method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.

In the present embodiment, a method of determining peak-to-valley periods of a power system operating on a mobile terminal, a computer terminal, or a similar computing device is provided, it being noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that herein.

Fig. 2 is a flowchart of a method of determining peak-to-valley periods of a power system according to an embodiment of the present application. As shown in fig. 2, the method comprises the steps of:

step S201, acquiring historical electricity consumption net load quantity, and determining a target time period according to the historical electricity consumption net load quantity, wherein the historical electricity consumption net load quantity comprises electricity consumption net load quantity of each historical time period in a plurality of historical time periods, the target time period comprises deep valley time periods and/or peak time periods, the deep valley time periods are the first n time periods in ascending order of the electricity consumption net load quantity in the historical electricity consumption net load quantity, and the peak time periods are the first n time periods in descending order of the electricity consumption net load quantity in the historical electricity consumption net load quantity;

Specifically, the target period may be a deep valley year or a peak year in the historical electricity net load, where the electricity consumption is increased due to the use of an air conditioner in summer, for example, the peak month may be 7 months; due to the less electricity consumption caused by the less 2 month date, 2 months of deep valley months may occur. The target time period may be selected one or more.

Step S202, obtaining the electricity consumption net load of each first sub-period of the target period, and determining a first target sub-period according to the electricity consumption net load of each first sub-period of the target period, wherein the first target sub-period comprises a first deep valley period and/or a first peak sub-period, the first target sub-period comprises the first deep valley period when the target period comprises the deep valley period, and the first target sub-period comprises the first peak sub-period when the target period comprises the peak period;

specifically, the first target sub-period may be a deep valley day or a peak day in the target period, and in the case that the target period is a deep valley month, that is, the deep valley day is determined according to the deep valley month; in the case where the target period is a spike month, that is, from the spike month, the spike day is determined. The number of the first target sub-time periods may be one or more.

Step S203, obtaining the electric payload amount of each second sub-period of the first sub-period, and determining a second target sub-period according to the electric payload amount of each second sub-period of the first sub-period, where the first target sub-period includes the second target sub-period, the second target sub-period includes a second deep valley period and/or a second peak sub-period, where the second target sub-period includes the second deep valley period when the first target sub-period includes the first deep valley period, and where the second target sub-period includes the second peak sub-period when the first target sub-period includes the first peak sub-period, the second target sub-period includes the second peak sub-period, and where the second target sub-period is used to adjust the electric price of the novel electric power system.

Specifically, the second target sub-period may be a deep valley period or a peak period in the first target sub-period, and in the case that the first target sub-period is a deep valley day, the deep valley period is determined according to the deep valley day; in case the first target sub-period is a spike day, i.e. according to the spike month, the spike period is determined. The number of the second target sub-time periods may be one or more. A clustering algorithm may be adopted to determine a second target sub-period according to the electric net load amount of each second sub-period of the first sub-period. The clustering algorithm can be C-means, hierarchical clustering or spectral clustering. The second target sub-time period is used for adjusting the electricity price of the novel electric power system, and is mainly used for representing that the current electricity load is extremely small under the condition that the second target sub-time period is a second deep millet time period, and the electricity price of the deep millet can be set, namely, the lower electricity price, so that a user can increase the electricity use, and further 'filling of the millet' is realized; under the condition that the second target sub-time period is the second peak sub-time period, the current power consumption is extremely large, and the user can reduce the power consumption by setting peak power price, namely higher power price, so that peak clipping is realized. The method can realize peak clipping and valley filling, and promote new energy consumption.

Through the embodiment, firstly, the historical electricity consumption payload quantity is obtained, and a target time period is determined according to the historical electricity consumption payload quantity; acquiring the electricity consumption net load of each first sub-time period of the target time period, and determining the first target sub-time period according to the electricity consumption net load of each first sub-time period of the target time period; and finally, acquiring the electricity consumption payload quantity of each second sub-time period of the first sub-time period, and determining a second target sub-time period according to the electricity consumption payload quantity of each second sub-time period of the first sub-time period. The electricity utilization net load of the novel power system is selected, peak Gu Yue and peak valley days are further selected, and then a plurality of peak valley time periods of the peak valley days are processed to determine the final peak valley time period, so that the actual supply and demand conditions can be reflected, and further the peak valley time period is determined. The problem that the supply and demand conditions of an actual novel power system cannot be truly reflected in the prior art is solved.

In a specific implementation process, the step S201 may be implemented by the following steps: step S2011 of acquiring a power generation load amount of the new power system, an external power generation load amount of the new power system, a power generation load amount of wind power generation of the new power system, a power generation load amount of photovoltaic power generation of the new power system, and an outgoing power generation load amount of the new power system; step S2012, according to the formula: l (L) _{NL_t} ＝L _{CL_t} -P _{ip_t} -P _{wi_t} -P _{S_t} +P _{OP_t} Determining the historical electricity consumption net load, wherein L _{NL_t} For the above-mentioned historical electric net load quantity, L _{CL_t} The power generation load of the novel power system is P _{ip_t} P is the external electric load of the novel electric power system _{wi_t} The power generation load amount P of the wind power generation of the novel power system _{S_t} The power generation load amount P of the photovoltaic power generation of the novel power system _{OP_t} And the load quantity is generated for the output of the novel power system.

Specifically, the method eliminates the external load amount in the power generation load amount of the novel power system, the power generation load amount of the wind power generation and the power generation load amount of the photovoltaic power generation, and the output power generation load is increased, so that the historical power utilization load after the influence of wind-solar new energy and provincial output curves is deducted from the novel power system is obtained. The method can rapidly acquire the historical electricity net load of the novel electric power system.

The step S202 may also be implemented in other manners, for example: step S2013, according to the formula: m is M _p ＝argsort(M ₁ ,M ₂ ,…,M ₁₂ ) Determining the target time period, wherein M _p For the above target period, argsort () is used for elements in the sorted array from small to large or from large to small, M _i For the above historical electric net load, i is 1 to 1 2.

Specifically, argsort () functions may be arranged from large to small or from small to large, and may be selected by those skilled in the art. The method can determine the size sequence of the target time period through the ordering function argsort (), and further quickly determine at least one deep valley month or peak month.

In order to further eliminate the influence of the climbing flexibility and the data noise in the first deep millet period, the step S202 of the present application may be implemented by: step S2021, determining that the minimum value of the electric power consumption payload amounts in the plurality of first sub-periods is the minimum payload amount; step S2022, determining that the condition is satisfied in the above target period:the first sub-period of (a) is the first deep millet period, wherein +_f>A power consumption net load amount at a first time of the first sub-period of the target period of time,/->A power consumption net load amount at a second time of the first sub-period of the target period of time,/->An amount of electricity payload, x, at a third time of the first sub-period of the target period _min Gamma, which is the minimum load ₁ The first time is a time preceding the second time, and the third time is a time following the second time, which is a first predetermined coefficient.

Specifically, in the method, the electric load is used at the moment of the first sub-time period, and the average value of the front moment and the rear moment of the electric load is obtained, so that the interference of the climbing flexibility of the novel electric power system can be further eliminated, and the influence caused by data errors can be reduced. A first sub-term satisfying the above formulaThe interval is the first deep valley time period, namely the deep valley day. Gamma ray ₁ Can be set according to the climate characteristics, and the value range can be 1-1.2.

To further eliminate the effect of the climbing flexibility and the data noise in the first peak sub-period, the step S202 may be implemented in other manners, for example: step S2023 of determining that the maximum value of the electric power consumption payload amounts in the plurality of first sub-periods is the maximum payload amount; step S2024, determining that the condition is satisfied in the above target period:the first sub-period of time is the first peak sub-period of time, wherein,a power consumption net load amount at a fourth time of the first sub-period of the target period of time, +.>A power consumption net load amount at a fifth time of the first sub-period of the target period of time,/->An amount of electricity payload x, which is a sixth time of the first sub-period of the target period _max For the maximum load amount, gamma ₂ The fourth time is a time preceding the fifth time, and the sixth time is a time following the fifth time, the second predetermined coefficient being the fourth predetermined coefficient.

Specifically, in the method, the electric load is used at the moment of the first sub-time period, and the average value of the front moment and the rear moment of the electric load is obtained, so that the interference of the climbing flexibility of the novel electric power system can be further eliminated, and the influence caused by data errors can be reduced. The first sub-period satisfying the above formula is the first peak period, i.e., the peak day. Gamma ray ₂ Can be set according to the climate characteristics, and the value range can be 0.8-1.

In some embodiments, the step S203 may be specifically performedThe method is realized by the following steps: step S2031, obtaining a matrix composed of the second sub-period of the first sub-periodWherein S is ⁿ For the matrix, ->The second sub-period being an mth of the first sub-period; step S2032, processing the matrix by adopting a differential evolution algorithm to obtain an initial clustering center of the matrix; and step S2033, clustering the matrix by adopting a fuzzy K-means algorithm according to the initial clustering center to obtain the second target sub-time period.

Specifically, after determining the deep valley day or the peak day, in order to adapt to the current demand side habit, the peak-valley period can be adjusted by the method. The second sub-period may be a valley period or a peak period. The differential evolution algorithm (Differential Evolution Algorithm, abbreviated as DE) is a global optimization algorithm, which is a group-based heuristic search algorithm, where each individual in a group corresponds to a solution vector. The evolution process of the differential evolution algorithm is very similar to that of the genetic algorithm, and comprises mutation, hybridization and selection operations. The fuzzy K-means algorithm is derived from the K-means algorithm. In the clustering process of the K-means algorithm, although the obtained result is not necessarily the expected effect, the boundaries among the categories are clear, and the clustering center is modified according to the samples of each type. In the clustering process of the fuzzy K-means algorithm, the class boundary obtained each time is still fuzzy, and all samples are needed for modifying the clustering center of each class. And selecting an initial clustering center by using a differential evolution algorithm, and then dividing the dynamic peak-valley time period by using a fuzzy K-means method according to the clustering center.

In order to further prevent the clustering process from being trapped in local optimization, in some embodiments, the step S2032 may be specifically implemented by the following steps: step S20321, generating a corresponding initial population according to each column element of the matrix, wherein the initial population comprises a plurality of initial individuals; step S20322, a mutation step, in which a mutation operation is performed on the initial population according to any of the initial individuals and the scaling factors in the initial population to obtain a plurality of mutated individuals; step S20323, a crossing step, in which crossing operation is performed on the variant individuals according to the variant individuals and the initial individuals, to obtain crossing individuals; a step S20324 of determining that the maximum value of the fitness function value of the intersecting individual and the fitness function value of the initial individual is a target value; and step S20325, in which when the target value is greater than the first threshold value, the mutation step, the crossover step, and the determination step are repeated at least once until the target value is equal to or less than the first threshold value, and the individual corresponding to the target value is determined to be the initial cluster center.

Specifically, the method can quickly determine the initial cluster center, and can further ensure the effectiveness of clusters falling into by time interval division.

In some embodiments, the step S2033 may be specifically implemented by the following steps: step S20331, calculating step, according toCalculating a criterion function of a membership matrix of the initial cluster center, wherein J _m (U, C) is the criterion function value of the membership matrix, and +.>A fuzzy matrix d (S) for the second sub-period of the first sub-period and the initial cluster center _j ,C _i ) M is the number of the second sub-time periods of the first sub-time period, and K is the number of the second target sub-time periods; step S20332, updating the initial cluster center and repeating the calculation step at least once when the criterion function value is equal to or greater than the second threshold value until the criterion function value is less than the second threshold value, and determiningAnd determining the data cluster corresponding to the clustering center corresponding to the criterion function as the second target sub-time period.

Specifically, the method may further divide the accurate second target sub-period according to the initial cluster center.

In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the method for determining the peak-to-valley period of the power system of the present application will be described in detail below with reference to specific embodiments.

The embodiment relates to a specific method for determining peak-to-valley period of a power system, as shown in fig. 3, since the prior art is limited to the study of peak period and electricity price, consumers are forced to transfer load demands by higher electricity price, which represents the value of electric quantity in the market of supply and demand, but is unfavorable for the consumption of new energy in the market of supply and demand and violates the market mechanism. Therefore, the embodiment determines a deep valley period of the novel power system, including the following steps:

step S1: collecting historical data and relevant prediction data of a certain province, and calculating a net load curve of a certain province for each month;

step S2: and selecting deep valley months according to the total monthly payload quantity, determining the maximum number of deep valley days per month to be 3, collecting peak-valley period division conditions of the deep valley months, and collecting power load prediction conditions. According to the formula: m is M _p ＝argsort(M ₁ ,M ₂ ,...,M ₁₂ ) Determining that the 6, 10 and 11 months are deep valley months;

step S3: determining the minimum daily load of deep valley month according to the prediction condition of the net electric load of the 6-month power system in this year, and determining the minimum daily load according to the daily load amount Wherein i represents the i-th day of the deep valley month; j represents the time of day, and the threshold value gamma is determined ₁ 1.15, the number of deep valley days is determined to be 5 according to +.>The deep valley days are calculated, and the time of day is calculated,the resulting (i=15, 16, 17, 20, 21) day may alternatively be a deep valley day;

step S4: after determining the selection of the deep valley days, selecting the deep valley period for the low valley period: recording the time sequence of the deep valley daily valley period and the corresponding payload quantity asn is a deep valley day mark, m is the total number of valley periods;

step S5: selecting an initial clustering center aiming at the matrix S, and selecting the initial clustering center by adopting a differential evolution algorithm in order to prevent the matrix S from sinking into local optimum: assuming that the population size is n=30, 2×30 times data are randomly selected to generate an initial population X (0) = { X ₁ (0),X ₂ (0),...,X _N (0) The number of the initial population can be 5-10 times of the number of the valley periods; calculation of initial population distance f (X) _i (t)), wherein t represents the number of iterations, and i represents the number of individuals; performing mutation operation, randomly selecting three different individuals from the initial population, and calculating V _i (t)＝X _a (t)+F(X _b (t)-X _c (t)) to obtain a variant, F is 0.9; the cross-over operation is performed and,namely, selecting a variant individual and a primary individual; a selection operation is performed to compare the function value f (X _i (t)) and f (U) _i (t)) determining the best individual to be the next generation population; iterating, checking whether the function value of the best individual in the previous step meets the requirement, outputting an initial clustering center if the function value of the best individual in the previous step meets the requirement, wherein the first threshold is 0.01>Otherwise, returning to the mutation operation for iteration;

step S6: according to the selected initial clustering center, a fuzzy K mean algorithm is used for carrying out time interval division and optimizing the problem:wherein d (S) _j ,C _i ) Is of special interestThe distance between the sign vector and the cluster center can be Manhattan distance, i.e. +.>Taking the blur coefficient a=2 and passing through the formula +.>Calculating to obtain a fuzzy matrix U, setting a classification number K=2, setting a fuzzy coefficient a=2, and initializing a membership matrix U= (U) _ij ) So thatAccording to the initial clustering center C ^N Calculating a new membership matrix; calculation J _m (U, C), if J _m (U, C) is less than the second threshold, ending the clustering process, otherwise updating the cluster center C with the new membership matrix ^N Iterating until a cluster center which finally meets the requirement is output, wherein the second threshold value can be 1 multiplied by 10 ^-6 ；

Step S7: finally, the division result of the deep valley period shown in table 1 is obtained.

TABLE 1 deep valley period partition results

Deep valley day	6 months 15 days	6 months and 16 days	6 month 17 day	6 months and 20 days	Day 21 of 6 months
						Deep valley period	6:00-8:00	4:00-7:00	4:00-7:00	5:00-8:00	4:00-7:00

The embodiment of the application also provides a peak-to-valley period determining device of the novel power system, and the peak-to-valley period determining device of the novel power system can be used for executing the peak-to-valley period determining method for the power system. The device is used for realizing the above embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

The following describes a device for determining peak-to-valley period of a power system according to an embodiment of the present application.

Fig. 4 is a schematic diagram of a determination device of peak-to-valley period of a power system according to an embodiment of the present application. As shown in fig. 4, the apparatus includes:

a first determining unit 10, configured to obtain a historical electricity payload amount, and determine a target period according to the historical electricity payload amount, where the historical electricity payload amount includes an electricity payload amount of each of a plurality of historical periods, and the target period includes a deep period and/or a peak period, where the deep period is a first n periods in ascending order of the electricity payload amounts in the historical electricity payload amounts, and the peak period is a first n periods in descending order of the electricity payload amounts in the historical electricity payload amounts;

Specifically, the target period may be deep valley months or peak months in the historical electricity net load, for example, the electricity consumption is increased due to the use of an air conditioner in summer, and the peak months may occur for 7 months; due to the less electricity consumption caused by the less 2 month date, 2 months of deep valley months may occur. The target time period may be selected one or more.

A second determining unit 20, configured to obtain an electricity payload amount of each first sub-period of the target period, and determine a first target sub-period according to the electricity payload amount of each first sub-period of the target period, where the first target sub-period includes a first deep valley period and/or a first peak sub-period, where the target period includes the deep valley period, and where the target period includes the peak period, the first target sub-period includes the first peak sub-period;

A third determining unit 30, configured to obtain an electric payload amount of each second sub-period of the first sub-period, and perform a clustering process on the electric payload amount of each second sub-period of the first sub-period, to determine a second target sub-period, where the first target sub-period includes the second target sub-period, the second target sub-period includes a second deep valley period and/or a second peak sub-period, where the second target sub-period includes the second deep valley period if the first target sub-period includes the first deep valley period, and where the second target sub-period includes the second peak sub-period if the first target sub-period includes the first peak sub-period, the second target sub-period is used to adjust an electric valence of a novel electric power system.

Specifically, the second target sub-period may be a deep valley period or a peak period in the first target sub-period, and in the case that the first target sub-period is a deep valley day, the deep valley period is determined according to the deep valley day; in case the first target sub-period is a spike day, i.e. according to the spike month, the spike period is determined. The number of the second target sub-time periods may be one or more. A clustering algorithm may be adopted to determine a second target sub-period according to the electric net load amount of each second sub-period of the first sub-period. The clustering algorithm can be C-means, hierarchical clustering or spectral clustering. The second target sub-time period is used for adjusting the electricity price of the novel electric power system, and is mainly used for representing that the current electricity load is extremely small under the condition that the second target sub-time period is a second deep millet time period, and the electricity price of the deep millet can be set, namely, the lower electricity price, so that a user can increase the electricity use, and further 'filling of the millet' is realized; under the condition that the second target sub-time period is the second peak sub-time period, the current power consumption is extremely large, and the user can reduce the power consumption by setting peak power price, namely higher power price, so that peak clipping is realized. The device can realize peak clipping and valley filling, and promote new energy consumption.

Through the embodiment, the first determining unit obtains the historical electricity consumption payload quantity, and determines the target time period according to the historical electricity consumption payload quantity; the second determining unit obtains the electricity consumption payload quantity of each first sub-time period of the target time period, and determines the first target sub-time period according to the electricity consumption payload quantity of each first sub-time period of the target time period; the third determining unit obtains the electricity consumption payload amount of each second sub-period of the first sub-period, and determines a second target sub-period according to the electricity consumption payload amount of each second sub-period of the first sub-period. The electricity utilization net load of the novel power system is selected, peak Gu Yue and peak valley days are further selected, and then a plurality of peak valley time periods of the peak valley days are processed to determine the final peak valley time period, so that the actual supply and demand conditions can be reflected, and further the peak valley time period is determined. The problem that the supply and demand conditions of an actual novel power system cannot be truly reflected in the prior art is solved.

In a specific implementation process, the first determining unit includes a first acquiring module and a first determining module, where the first acquiring module is configured to acquire a power generation load of the novel power system, an external power load of the novel power system, a power generation load of wind power generation of the novel power system, a power generation load of photovoltaic power generation of the novel power system, and an outgoing power generation load of the novel power system; the first determining module is used for determining the following formula L _{NL_t} ＝L _{CL_t} -P _{ip_t} -P _{wi_t} -P _{S_t} +P _{OP_t} Determining the historical electricity consumption net load, wherein L _{NL_t} For the above-mentioned historical electric net load quantity, L _{CL_t} The power generation load of the novel power system is P _{ip_t} P is the external electric load of the novel electric power system _{wi_t} The power generation load amount P of the wind power generation of the novel power system _{S_t} The power generation load amount P of the photovoltaic power generation of the novel power system _{OP_t} And the load quantity is generated for the output of the novel power system.

Specifically, the device obtains the historical electricity net load of the novel power system after deducting the influence of wind-solar new energy and the provincial output curve by eliminating the external electric load in the electricity generation load of the novel power system, the electricity generation load of the wind power generation and the electricity generation load of the photovoltaic power generation and increasing the output electricity generation load. The device can rapidly acquire the historical electricity net load of the novel electric power system.

The second determining unit is further configured to determine a second value according to formula M _p ＝argsort(M ₁ ,M ₂ ,...,M ₁₂ ) Determining the target time period, wherein M _p For the above target period, argsort () is used for elements in the sorted array from small to large or from large to small, M _i For the above historical electric payload amount, i is any integer value from 1 to 12.

Specifically, the device can determine the size sequence of the target time periods through the ranking function argsort (), and further quickly determine at least one deep valley month or peak month.

In order to further eliminate the influence of the climbing flexibility and the data noise of the first deep millet time period, the second determining unit of the present application includes a second determining module and a third determining module, where the second determining module is configured to determine that a minimum value of the electric net load amounts in the plurality of first sub time periods is a minimum load amount; the third determining module is configured to determine that the target period satisfies a condition:the first sub-period of (a) is the first deep millet period, wherein +_f>For the amount of payload of electricity used at the first time of the first sub-period of the target period,a power consumption net load amount at a second time of the first sub-period of the target period of time,/->An amount of electricity payload, x, at a third time of the first sub-period of the target period _min Gamma, which is the minimum load ₁ The first time is a time preceding the second time, and the third time is a time following the second time, which is a first predetermined coefficient.

Specifically, in the device, the electric load is used at the moment of the first sub-time period, and the average value of the front moment and the rear moment of the electric load is obtained, so that the interference of the climbing flexibility of the novel electric power system can be further eliminated, and the influence caused by data errors can be reduced. The first sub-period satisfying the above formula is the first deep valley period, i.e., the deep valley day. Gamma ray ₁ Can be set according to the climate characteristics, and the value range can be1 to 1.2.

In order to further eliminate the influence of the climbing flexibility and the data noise of the first peak sub-period, the second determining unit includes a fourth determining module and a fifth determining module, where the fourth determining module is configured to determine that a maximum value of the electric power consumption payload amounts in the plurality of first sub-periods is a maximum payload amount; the fifth determining module is configured to determine that the target period satisfies a condition:the first sub-period of (2) is the first peak sub-period, wherein +_>A power consumption net load amount at a fourth time of the first sub-period of the target period of time, +.>A power consumption net load amount at a fifth time of the first sub-period of the target period of time,/->An amount of electricity payload x, which is a sixth time of the first sub-period of the target period _max For the maximum load amount, gamma ₂ The fourth time is a time preceding the fifth time, and the sixth time is a time following the fifth time, the second predetermined coefficient being the fourth predetermined coefficient.

Specifically, in the device, the electric load is used at the moment of the first sub-time period, and the average value of the front moment and the rear moment of the electric load is obtained, so that the interference of the climbing flexibility of the novel electric power system can be further eliminated, and the influence caused by data errors can be reduced. The first sub-period satisfying the above formula is the first peak period, i.e., the peak day. Gamma ray ₂ Can be set according to the climate characteristics, and the value range can be 0.8-1.

In some embodiments, the third determining unit includes a second acquiring module, a first processing module and a second processing module,wherein the second acquisition module is configured to acquire a matrix composed of the second sub-period of the first sub-periodWherein S is ⁿ For the matrix, ->The second sub-period being an mth of the first sub-period; the first processing module is used for processing the matrix by adopting a differential evolution algorithm to obtain an initial clustering center of the matrix; the first processing module is used for carrying out clustering processing on the matrix by adopting a fuzzy K mean value algorithm according to the initial clustering center to obtain the second target sub-time period.

Specifically, in the device, after the deep valley day or peak day is determined, in order to adapt to the current demand side habit, the device can adjust the peak-valley period. The second sub-period may be a valley period or a peak period. The differential evolution algorithm (Differential Evolution Algorithm, DE for short) is a global optimization algorithm, which is a heuristic search algorithm based on a group, and each individual in the group corresponds to a solution vector. The evolution process of the differential evolution algorithm is very similar to that of the genetic algorithm, and comprises mutation, hybridization and selection operations. The fuzzy K-means algorithm is derived from the K-means algorithm. In the clustering process of the K-means algorithm, although the obtained result is not necessarily the expected effect, the boundaries among the categories are clear, and the clustering center is modified according to the samples of each type. In the clustering process of the fuzzy K-means algorithm, the class boundary obtained each time is still fuzzy, and all samples are needed for modifying the clustering center of each class. And selecting an initial clustering center by using a differential evolution algorithm, and then dividing the dynamic peak-valley time period by using a fuzzy K-means method according to the clustering center.

In order to further prevent the clustering process from being trapped in local optimization, in some embodiments, the first processing module includes a first processing sub-module, a second processing sub-module, a third processing sub-module, a determining sub-module, and a repeating sub-module, where the first processing sub-module is configured to generate a corresponding initial population according to each column element of the matrix, and the initial population includes a plurality of initial individuals; the second processing sub-module is used for performing mutation operation on the initial population according to any initial individuals and scaling factors in the initial population to obtain a plurality of mutated individuals; the third processing sub-module is used for intersecting the variant individuals according to the variant individuals and the initial individuals to obtain intersecting individuals; the determining submodule is used for determining that the maximum value of the fitness function value of the crossed individual and the fitness function value of the initial individual is a target value; and the repetition submodule is used for repeating the mutation step, the crossover step and the determination step at least once when the target value is larger than a first threshold value until the target value is smaller than or equal to the first threshold value, and determining that the individual corresponding to the target value is the initial clustering center.

Specifically, the device can quickly determine the initial clustering center, and can further ensure the effectiveness of clusters in which time interval division falls.

In some embodiments, the second processing module includes a calculation sub-module and an update sub-module, where the calculation sub-module is used for calculating according to the following stepsCalculating a criterion function of a membership matrix of the initial cluster center, wherein J _m (U, C) is the criterion function value of the membership matrix, and +.>A fuzzy matrix d (S) for the second sub-period of the first sub-period and the initial cluster center _j ,C _i ) For the distance between the second sub-period of the first sub-period and the initial clustering center, m is the number of the second sub-periods of the first sub-period, K is the number of the second target sub-periods, and a is the fuzzy matrixA blur coefficient; the updating sub-module is configured to update the initial cluster center and repeat the calculating step at least once when the criterion function value is greater than or equal to a second threshold value until the criterion function value is less than the second threshold value, and determine that a data cluster corresponding to the cluster center corresponding to the criterion function is the second target sub-period.

Specifically, the apparatus may further divide the accurate second target sub-period according to the initial cluster center.

The peak-to-valley period determining device of the novel power system comprises a processor and a memory, wherein the first determining unit, the second determining unit, the third determining unit and the like are all stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions. The modules are all located in the same processor; alternatively, the above modules may be located in different processors in any combination.

The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel may be provided with one or more peak-to-valley periods determined by adjusting the kernel parameters.

The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.

The embodiment of the invention provides a computer readable storage medium, which comprises a stored program, wherein the program is controlled to control equipment where the computer readable storage medium is located to execute a method for determining peak-to-valley period of an electric power system.

Specifically, the method for determining the peak-to-valley period of the power system includes:

The embodiment of the invention provides a processor, which is used for running a program, wherein the program runs to execute the method for determining the peak-to-valley period of the power system.

The embodiment of the invention provides equipment, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the processor realizes at least the following steps when executing the program:

The device herein may be a server, PC, PAD, cell phone, etc.

The application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with at least the following method steps:

It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.

From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

1) Firstly, acquiring historical electricity consumption net load quantity, and determining a target time period according to the historical electricity consumption net load quantity; acquiring the electricity consumption net load of each first sub-time period of the target time period, and determining the first target sub-time period according to the electricity consumption net load of each first sub-time period of the target time period; and finally, acquiring the electricity consumption payload quantity of each second sub-time period of the first sub-time period, and determining a second target sub-time period according to the electricity consumption payload quantity of each second sub-time period of the first sub-time period. The electricity utilization net load of the novel power system is selected, peak Gu Yue and peak valley days are further selected, and then a plurality of peak valley time periods of the peak valley days are processed to determine the final peak valley time period, so that the actual supply and demand conditions can be reflected, and further the peak valley time period is determined. The problem that the supply and demand conditions of an actual novel power system cannot be truly reflected in the prior art is solved.

2) The peak-valley period determining device of the novel power system comprises a first determining unit, a second determining unit and a third determining unit, wherein the first determining unit obtains historical electricity consumption net load quantity and determines a target period according to the historical electricity consumption net load quantity; the second determining unit obtains the electricity consumption payload quantity of each first sub-time period of the target time period, and determines the first target sub-time period according to the electricity consumption payload quantity of each first sub-time period of the target time period; the third determining unit obtains the electricity consumption payload amount of each second sub-period of the first sub-period, and determines a second target sub-period according to the electricity consumption payload amount of each second sub-period of the first sub-period. The electricity utilization net load of the novel power system is selected, peak Gu Yue and peak valley days are further selected, and then a plurality of peak valley time periods of the peak valley days are processed to determine the final peak valley time period, so that the actual supply and demand conditions can be reflected, and further the peak valley time period is determined. The problem that the supply and demand conditions of an actual novel power system cannot be truly reflected in the prior art is solved.

The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for determining peak-to-valley time periods of an electrical power system, comprising:

acquiring historical electricity consumption payload quantity, and determining a target time period according to the historical electricity consumption payload quantity, wherein the historical electricity consumption payload quantity comprises electricity consumption payload quantity of each historical time period in a plurality of historical time periods, the target time period comprises deep time periods and/or peak time periods, the deep time periods are the first n time periods in ascending order of the electricity consumption payload quantity in the historical electricity consumption payload quantity, and the peak time periods are the first n time periods in descending order of the electricity consumption payload quantity in the historical electricity consumption payload quantity;

acquiring the electricity consumption payload quantity of each first sub-period of the target period, and determining a first target sub-period according to the electricity consumption payload quantity of each first sub-period of the target period, wherein the first target sub-period comprises a first deep valley period and/or a first peak sub-period, the first target sub-period comprises the first deep valley period when the target period comprises the deep valley period, and the first target sub-period comprises the first peak sub-period when the target period comprises the peak period;

Obtaining the electric payload amount of each second sub-period of the first sub-period, and determining a second target sub-period according to the electric payload amount of each second sub-period of the first sub-period, wherein the first target sub-period comprises the second target sub-period, the second target sub-period comprises a second deep valley period and/or a second peak sub-period, the second target sub-period comprises the second deep valley period when the first target sub-period comprises the first deep valley period, the second target sub-period comprises the second peak sub-period when the first target sub-period comprises the first peak sub-period, and the second target sub-period is used for adjusting the electric price of a novel electric power system.

2. The method of claim 1, wherein obtaining historical electrical payload comprises:

acquiring the power generation load capacity of the novel power system, the external power generation load capacity of the novel power system, the power generation load capacity of the wind power generation of the novel power system, the power generation load capacity of the photovoltaic power generation of the novel power system and the power generation load capacity of the novel power system;

According to the formula: l (L) _{NL_t} ＝L _{CL_t} -P _{ip_t} -P _{wi_t} -P _{S_t} +P _{OP_t} Determining the historical electricity payload quantity, wherein L _{NL_t} For said historical electric payload, L _{CL_t} For the power generation load of the novel power system, P _{ip_t} For the external electric load quantity of the novel electric power system, P _{wi_t} Wind power for the novel power systemPower generation load amount, P of power generation _{S_t} Power generation load amount, P, for photovoltaic power generation of the novel power system _{OP_t} And generating load quantity for the output of the novel power system.

3. The method of claim 1, wherein determining a target time period based on the historical electrical payload amount comprises:

according to formula M _p ＝argsort(M ₁ ,M ₂ ,...,M ₁₂ ) Determining the target time period, wherein M _p For the target time period, argsort () is used to sort the elements in the array from small to large or from large to small, M _i For the historical electrical payload, i is any integer value from 1 to 12.

4. The method of claim 1, wherein determining the first deep millet period based on the amount of electrical payload for each first sub-period of the target period comprises:

determining the minimum value of the electric net load in a plurality of first sub-time periods as the minimum load;

Determining that the target time period satisfies a condition:is said first deep millet period, wherein +.f.>For the amount of payload of electricity at a first moment of said first sub-period of said target period of time,/for a first time period of said target period of time>For the amount of payload of electricity at the second moment of said first sub-period of said target period of time,/or->An amount of payload of electricity x, which is a third time instant of the first sub-period of the target period of time _min Gamma, the minimum load ₁ The first time is a time before the second time, and the third time is a time next to the second time, which is a first predetermined coefficient.

5. The method of claim 1, wherein determining the first peak sub-period based on the amount of payload of electricity for each first sub-period of the target period comprises:

determining the maximum value of the electric net load in a plurality of first sub-time periods as the maximum load;

determining that the target time period satisfies a condition:is the first peak sub-period, wherein +_f->For the amount of payload of electricity at a fourth time instant of said first sub-period of said target period of time,/for a fourth time instant of said first sub-period of said target period of time >For the amount of payload of electricity at the fifth moment of said first sub-period of said target period of time,/for the first sub-period of time>An amount of electric net load, x, at a sixth time instant of the first sub-period of the target period _max For the maximum load amount, gamma ₂ The fourth time is a time preceding the fifth time, and the sixth time is a time next to the fifth time, which is a second predetermined coefficient.

6. A method according to any one of claims 1 to 3, wherein determining a second target sub-period from the amount of payload for each second sub-period of the first sub-period comprises:

acquiring a matrix composed of the second sub-time periods of the first sub-time periodWherein S is ⁿ For the matrix>The second sub-period being an mth of the first sub-period;

processing the matrix by adopting a differential evolution algorithm to obtain an initial clustering center of the matrix;

and clustering the matrix by adopting a fuzzy K mean algorithm according to the initial clustering center to obtain the second target sub-time period.

7. The method of claim 6, wherein processing the matrix using a differential evolution algorithm results in an initial cluster center for the matrix, comprising:

Generating, namely generating a corresponding initial population according to each column element of the matrix, wherein the initial population comprises a plurality of initial individuals;

a mutation step of performing mutation operation on the initial population according to any initial individual and scaling factors in the initial population to obtain a plurality of mutated individuals;

a crossing step, according to the variant individuals and the initial individuals, carrying out crossing operation on the variant individuals to obtain crossed individuals;

determining, namely determining the maximum value of the fitness function value of the crossed individual and the fitness function value of the initial individual as a target value;

and repeating the mutation step, the crossing step and the determining step at least once under the condition that the target value is larger than a first threshold value until the target value is smaller than or equal to the first threshold value, and determining that the individual corresponding to the target value is the initial clustering center.

8. The method of claim 6, wherein clustering the matrix using a fuzzy K-means algorithm according to the initial cluster center to obtain the second target sub-time period comprises:

a calculation step according to Calculating a criterion function of a membership matrix of the initial cluster center, wherein J _m (U, C) is a criterion function value of the membership matrix,/and>d (S) is a fuzzy matrix of the second sub-period of the first sub-period and the initial cluster center _j ,C _i ) M is the number of the second sub-time periods of the first sub-time period and K is the number of the second target sub-time periods;

and under the condition that the criterion function value is greater than or equal to a second threshold value, updating the initial clustering center and repeating the calculating step at least once until the criterion function value is smaller than the second threshold value, and determining a data cluster corresponding to the clustering center corresponding to the criterion function as the second target sub-time period.

9. A peak-to-valley period determination device of an electric power system, characterized by comprising:

a first determining unit, configured to obtain a historical electricity payload amount, and determine a target time period according to the historical electricity payload amount, where the historical electricity payload amount includes an electricity payload amount of each of a plurality of historical time periods, the target time period includes a deep valley time period and/or a peak time period, the deep valley time period is a first n time periods in ascending order of the electricity payload amounts in the historical electricity payload amounts, and the peak time period is a first n time periods in descending order of the electricity payload amounts in the historical electricity payload amounts;

A second determining unit, configured to obtain an electricity payload amount of each first sub-period of the target period, and determine a first target sub-period according to the electricity payload amount of each first sub-period of the target period, where the first target sub-period includes a first deep valley period and/or a first peak sub-period, where the first target sub-period includes the first deep valley period if the target period includes the deep valley period, and where the first target sub-period includes the first peak sub-period if the target period includes the peak period;

a third determining unit, configured to obtain an electricity consumption payload amount of each second sub-period of the first sub-period, and perform clustering processing on the electricity consumption payload amount of each second sub-period of the first sub-period, and determine a second target sub-period, where the first target sub-period includes the second target sub-period, the second target sub-period includes a second deep valley period and/or a second peak sub-period, where the second target sub-period includes the second deep valley period when the first target sub-period includes the first deep valley period, and where the second target sub-period includes the second peak sub-period when the first target sub-period includes the first peak sub-period, the second target sub-period is used to adjust an electricity price of a novel electric power system.

10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored program, wherein the program when run controls a device in which the computer readable storage medium is located to perform the method according to any one of claims 1 to 8.

11. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to perform the method of any of claims 1 to 8 by means of the computer program.