CN114069644B - Power demand response method, system, medium and equipment based on data matching algorithm - Google Patents

Power demand response method, system, medium and equipment based on data matching algorithm Download PDF

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Publication number
CN114069644B
CN114069644B CN202111475355.2A CN202111475355A CN114069644B CN 114069644 B CN114069644 B CN 114069644B CN 202111475355 A CN202111475355 A CN 202111475355A CN 114069644 B CN114069644 B CN 114069644B
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China
Prior art keywords
power
data
demand response
energy service
response
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CN202111475355.2A
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CN114069644A (en
Inventor
孙洪山
陈啸
刘荣磊
庞大鹏
翟顾丽
任兴星
陈雷
雷超
高丽红
张晓旭
毕研文
蔚宁
刘娟
贾环环
陈旻娜
徐潇
胡敬敬
焦嵩
张淑秀
开万欣
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State Grid Shandong Electric Power Co Wenshang Power Supply Co
State Grid Corp of China SGCC
Jining Power Supply Co
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State Grid Shandong Electric Power Co Wenshang Power Supply Co
State Grid Corp of China SGCC
Jining Power Supply Co
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Priority to CN202111475355.2A priority Critical patent/CN114069644B/en
Publication of CN114069644A publication Critical patent/CN114069644A/en
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/16—Energy services, e.g. dispersed generation or demand or load or energy savings aggregation

Abstract

The application discloses a power demand response method, a system, a medium and equipment based on a data matching algorithm, which are used for carrying out information matching on energy service providers and users in the same grade by grading the energy service providers and the users, carrying out priority feedback according to the importance degree of response information in the electricity consumption peak period, solving the problem that the current power demand response technology cannot complete urgent information interaction on important users and the energy service providers in the first time of the electricity consumption peak period, avoiding the repeated transmission and missed transmission of information in the demand response period, preventing the repeated response of the power users and ensuring the stable operation of a power system.

Description

Power demand response method, system, medium and equipment based on data matching algorithm
Technical Field
The application relates to the technical field of power demand response, in particular to a power demand response method, a system, a medium and equipment based on a data matching algorithm.
Background
The power demand response means that when the power consumption peak period or the system safety reliability is at risk, the power user voluntarily selects to reduce or increase the power consumption load in a certain period according to the power consumption price signal or the excitation mechanism, so that the power supply and demand balance is promoted, and the system safety and reliability operation is ensured.
However, due to diversification of users and energy service providers, information interaction realized by the existing power demand response technology is more and more complex, and along with continuous increase of power load, if rapid response and power adjustment cannot be performed on user will in a power consumption peak period, the stability of the whole power supply system can be affected.
Disclosure of Invention
Aiming at the defects existing in the prior art, the application aims to provide a power demand response method, a system, a medium and equipment based on a data matching algorithm, which are used for carrying out information matching on the same-level energy service providers and users by classifying the energy service providers and the users, carrying out priority feedback according to the importance degree of response information in the electricity utilization peak period, and solving the problem that the current power demand response technology cannot complete urgent information interaction for important users and energy service providers in the first time of the electricity utilization peak period.
In order to achieve the above object, the present application is realized by the following technical scheme:
the first aspect of the present disclosure provides a power demand response method based on a data matching algorithm, including the steps of:
acquiring electricity data of power users and energy service providers;
the method comprises the steps of utilizing electricity consumption data of power users and energy service providers to conduct data mining, and grading the power users and the energy service providers;
forming a demand response matching pair for the power users and the energy service providers at the same level through a data matching algorithm;
during the demand response period, receiving a demand response request signal sent by an electric energy supply service provider, and classifying according to the emergency degree of the signal, wherein the signal is divided into emergency information and non-emergency information;
information transmission is carried out according to the emergency degree of the signal, a demand response request scheme is determined according to the matching pair, and power scheduling is carried out;
after the demand response is finished, historical electricity consumption data is obtained from the electricity users participating in the demand response, an electricity consumption baseline is determined, the electricity consumption data and the baseline data during the demand response are compared, the demand response effect is evaluated, and feedback is carried out.
Preferably, the power consumer includes industrial enterprises, commercial establishments and residents.
Preferably, the data acquisition is connected with an energy management system at the power consumer side or an energy service platform of a power grid company through the Internet, and the historical response data of the power consumer and the historical power utilization data of an energy service provider are acquired in a timing polling mode.
Preferably, the power utilization data of the power users and the energy service providers are utilized for data mining, the proportion coefficient of each data is set according to the data mining result, and the priority value is calculated;
the grading of the power consumer and the energy service provider comprises: high-grade power consumer, low-grade power consumer, high-grade energy service provider, and low-grade energy service provider.
Preferably, the process of forming the corresponding matching pair of the requirements for the power users and the energy service providers at the same level through a data matching algorithm is as follows:
and extracting the characteristics of the power utilization data of the power user, correspondingly generating a power utilization data characteristic library, and calculating the matching probability of the power user and the energy service provider through the extracted characteristics. And forming a demand response matching pair according to the matching probability of the power user and the energy service provider.
Preferably, the emergency information prioritizes notification of advanced power users;
the non-urgent information informs all power users according to the pairing principle.
Preferably, when the demand response effect is evaluated and feedback is performed, the pairing situation among the high-level power consumer, the low-level power consumer, the high-level energy service provider and the low-level energy service provider is re-determined.
A second aspect of the present disclosure provides a power demand response system based on a data matching algorithm, comprising: the system comprises a data acquisition module, a demand response potential analysis module, a demand response information classification module, a demand response service execution module and a demand response effect evaluation module.
The data acquisition module is used for acquiring electricity utilization data of the power users and the energy service providers.
The demand response potential analysis module is used for carrying out data mining by utilizing electricity data of the power users and the energy service providers and grading the power users and the energy service providers;
or, the demand response matching pair is formed for the power users and the energy service providers of the same level through a data matching algorithm.
The demand response information classification module is used for receiving a demand response request signal sent by a power supply service provider during a demand response period, classifying according to the emergency degree of the signal, and classifying into emergency information and non-emergency information.
And the demand response service execution module is used for carrying out information transfer according to the emergency degree of the signal, determining a demand response request scheme according to the matched pair and executing power dispatching.
The demand response effect evaluation module is used for acquiring historical electricity consumption data from the electricity users participating in demand response after the demand response is finished, determining an electricity consumption baseline, comparing the electricity consumption data and the baseline data during the demand response, evaluating the demand response effect, feeding back to the demand response potential analysis module, and re-judging the pairing situation among the high-grade electricity users, the low-grade electricity users, the high-grade energy service provider, the low-grade energy service provider and the high-grade energy service provider.
A third aspect of the present disclosure provides a medium having stored thereon a program which when executed by a processor implements the steps in a data matching algorithm based power demand response method as described in the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an apparatus comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the steps in the data matching algorithm based power demand response method according to the first aspect of the present disclosure when the program is executed.
The beneficial effects of the embodiment of the application are as follows:
the utility model provides a power demand response method, system, medium and equipment based on data matching algorithm, through classifying energy service provider and electric power user, guarantee in the power demand response period, the priority transmits the electric power demand of the energy service provider that the electric power demand is big, compensation dynamics is big to the priority adjusts big electric power user of application electric quantity, for the electric power supply and demand regulation during the electric power demand response period has practiced thrift the time, satisfies the corresponding real-time requirement of demand, avoids economy, the energy loss because of supplying power for a long time is not enough.
The method and the system are based on a grading system, so that the energy service providers and the power users are graded, the repeated sending and missed sending of information during the demand response period is avoided, the repeated response of the power users is prevented, and the stable operation of the power system is ensured.
The utility model provides a power demand response method, system, medium and equipment based on data matching algorithm, combines with pairing algorithm through hierarchical system, when carrying out information interaction, the priority is transmitted urgent information to advanced power consumer, and non-urgent information is transmitted according to the pairing principle, has increased the probability of demand response to can take more efficient power scheduling scheme, improve demand response service quality, realize "peak clipping and valley filling" fast.
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.
FIG. 1 is a flow chart of a demand response method according to the present application.
The specific embodiment is as follows:
it should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof;
embodiment one:
an embodiment of the present disclosure provides a power demand response method based on a data matching algorithm, including the following steps:
acquiring electricity data of power users and energy service providers;
the method comprises the steps of utilizing electricity consumption data of power users and energy service providers to conduct data mining, and grading the power users and the energy service providers;
forming a demand response matching pair for the power users and the energy service providers at the same level through a data matching algorithm;
during the demand response period, receiving a demand response request signal sent by an electric energy supply service provider, and classifying according to the emergency degree of the signal, wherein the signal is divided into emergency information and non-emergency information;
information transmission is carried out according to the emergency degree of the signal, a demand response request scheme is determined according to the matching pair, and power scheduling is carried out;
after the demand response is finished, historical electricity consumption data is obtained from the electricity users participating in the demand response, an electricity consumption baseline is determined, the electricity consumption data and the baseline data during the demand response are compared, the demand response effect is evaluated, and feedback is carried out.
Preferably, the power consumer includes industrial enterprises, commercial establishments and residents.
Preferably, the data acquisition is connected with an energy management system at the power consumer side or an energy service platform of a power grid company through the Internet, and the historical response data of the power consumer and the historical power utilization data of an energy service provider are acquired in a timing polling mode.
Preferably, the data mining is performed by using electricity data of the electricity consumers and the energy service providers, wherein the electricity data comprises electricity data of the electricity consumers and the energy service commercial electric load equipment or a plurality of equipment aggregate electric lines, the data comprises price, load, times, duration and the like, and the data acquisition frequency is usually once every 15 minutes.
Setting a proportionality coefficient of each data according to the data mining result, and according to the formula:
calculating a priority value Q i The method comprises the steps of carrying out a first treatment on the surface of the Wherein b 1 、b 2 All are proportional coefficients obtained according to the digging result, L i For the load variation, P i For the length of the demand response time, n is the number of power demand response times, the power consumer and the energy service provider are marked as i, i is a positive integer, and mu is a correction factor.
The grading of the power consumer and the energy service provider comprises: high-grade power consumer, low-grade power consumer, high-grade energy service provider, and low-grade energy service provider.
The power users with the priority values in the range of the high-level response threshold values are high-level power users, and the rest are low-level power users;
the energy service providers with the priority values within the range of the high-level demand threshold are high-level energy service providers, and the rest are low-level energy service providers.
The high-level response threshold range and the high-level demand threshold range can be set by a system manager according to actual conditions.
Preferably, the process of forming the corresponding matching pair of the requirements for the power users and the energy service providers at the same level through a data matching algorithm is as follows:
and extracting the characteristics of the power utilization data of the power user, correspondingly generating a power utilization data characteristic library, and calculating the matching probability of the power user and the energy service provider through the extracted characteristics. And forming a demand response matching pair according to the matching probability of the power user and the energy service provider. The method comprises the following specific steps:
extracting characteristics of electricity consumption data of the power users, generating an electricity consumption data characteristic library, and calculating matching probability of the power users and energy service providers through the extracted characteristics:
the collected power consumer electricity consumption data are classified into power response times data, power response price data, power response duration data and power response load data, and four power consumer electricity consumption indexes are respectively corresponding to the four power consumer electricity consumption indexes: the method comprises the steps of performing power response times, power response prices, power response duration and power response load, and grouping classified data to obtain K groups of data;
extracting features of the obtained K groups of data to obtain K groups of feature vectors, and obtaining a power utilization data feature library;
1/n is randomly selected from all feature vectors of the electricity utilization data feature library in a random resampling mode, a sliding average value is taken as a reference feature vector of electricity utilization data of each kind of electricity utilization user, and the formula of the reference feature vector is as follows:
wherein m=1, 2, …,4 represents 4 kinds of power consumer power consumption data, i is the number of selected feature vectors under the m-th power consumer power consumption data;
classifying and identifying four kinds of power consumer power consumption data by adopting a majority voting judgment mode:
after the reference feature vectors of the four power consumer power consumption data are obtained, the feature vectors of the commercial power data of the energy service to be judged are respectively compared with the four reference feature vectors, and the data to be judged are power demand frequency data, power demand price data, power demand duration data and power demand load data, which correspond to the power respectivelyThe method comprises the steps of obtaining four absolute distances from response frequency data, electric power response price data, electric power response duration data and electric power response load data, obtaining the minimum value of the four absolute distances, casting one vote of electric power utilization data corresponding to the minimum value, carrying out voting judgment on each element of the commercial electric power data of the energy service to be judged, and outputting the electric power user power utilization index category with the highest vote as a judgment result; setting variable P toc Representing the matching probability of the power user and the energy service provider, wherein the initial value is 1, and P is changed according to the judgment result of the power utilization index of the power user toc Values. And if the matching probability is higher than the set value, forming a corresponding matching pair of the requirements.
Preferably, the emergency information prioritizes notification of advanced power users;
the non-urgent information informs all power users according to the pairing principle.
Preferably, when the demand response effect is evaluated and feedback is performed, the pairing situation among the high-level power consumer, the low-level power consumer, the high-level energy service provider and the low-level energy service provider is re-determined.
Embodiment two:
an embodiment II of the present disclosure provides a power demand response system based on a data matching algorithm, including: the system comprises a data acquisition module, a demand response potential analysis module, a demand response information classification module, a demand response service execution module and a demand response effect evaluation module.
The data acquisition module is used for acquiring electricity utilization data of the power users and the energy service providers.
The demand response potential analysis module is used for carrying out data mining by utilizing electricity data of the power users and the energy service providers and grading the power users and the energy service providers;
or, the demand response matching pair is formed for the power users and the energy service providers of the same level through a data matching algorithm.
The demand response information classification module is used for receiving a demand response request signal sent by a power supply service provider during a demand response period, classifying according to the emergency degree of the signal, and classifying into emergency information and non-emergency information.
And the demand response service execution module is used for carrying out information transfer according to the emergency degree of the signal, determining a demand response request scheme according to the matched pair and executing power dispatching.
The demand response effect evaluation module is used for acquiring historical electricity consumption data from the electricity users participating in demand response after the demand response is finished, determining an electricity consumption baseline, comparing the electricity consumption data and the baseline data during the demand response, evaluating the demand response effect, feeding back to the demand response potential analysis module, and re-judging the pairing situation among the high-grade electricity users, the low-grade electricity users, the high-grade energy service provider, the low-grade energy service provider and the high-grade energy service provider.
Embodiment III:
a third embodiment of the present disclosure provides a medium having a program stored thereon, which when executed by a processor, implements the steps in the power demand response method based on the data matching algorithm according to the first embodiment of the present disclosure, where the steps are:
acquiring electricity data of power users and energy service providers;
the method comprises the steps of utilizing electricity consumption data of power users and energy service providers to conduct data mining, and grading the power users and the energy service providers;
forming a demand response matching pair for the power users and the energy service providers at the same level through a data matching algorithm;
during the demand response period, receiving a demand response request signal sent by an electric energy supply service provider, and classifying according to the emergency degree of the signal, wherein the signal is divided into emergency information and non-emergency information;
information transmission is carried out according to the emergency degree of the signal, a demand response request scheme is determined according to the matching pair, and power scheduling is carried out;
after the demand response is finished, historical electricity consumption data is obtained from the electricity users participating in the demand response, an electricity consumption baseline is determined, the electricity consumption data and the baseline data during the demand response are compared, the demand response effect is evaluated, and feedback is carried out.
The detailed steps are the same as those of the power demand response method based on the data matching algorithm provided in the first embodiment, and will not be repeated here.
Embodiment four:
a fourth embodiment of the present disclosure provides an apparatus, including a memory, a processor, and a program stored on the memory and executable on the processor, where the processor implements steps in the power demand response method based on the data matching algorithm according to the first embodiment of the present disclosure when executing the program, where the steps are:
acquiring electricity data of power users and energy service providers;
the method comprises the steps of utilizing electricity consumption data of power users and energy service providers to conduct data mining, and grading the power users and the energy service providers;
forming a demand response matching pair for the power users and the energy service providers at the same level through a data matching algorithm;
during the demand response period, receiving a demand response request signal sent by an electric energy supply service provider, and classifying according to the emergency degree of the signal, wherein the signal is divided into emergency information and non-emergency information;
information transmission is carried out according to the emergency degree of the signal, a demand response request scheme is determined according to the matching pair, and power scheduling is carried out;
after the demand response is finished, historical electricity consumption data is obtained from the electricity users participating in the demand response, an electricity consumption baseline is determined, the electricity consumption data and the baseline data during the demand response are compared, the demand response effect is evaluated, and feedback is carried out.
The detailed steps are the same as those of the power demand response method based on the data matching algorithm provided in the first embodiment, and will not be repeated here.
It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. 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.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random access Memory (Random AccessMemory, RAM), or the like.
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 (7)

1. A power demand response method based on a data matching algorithm, comprising:
acquiring electricity data of power users and energy service providers;
the method comprises the steps of utilizing electricity consumption data of power users and energy service providers to conduct data mining, and grading the power users and the energy service providers;
forming a demand response matching pair for the power users and the energy service providers at the same level through a data matching algorithm;
during the demand response period, receiving a demand response request signal sent by an electric energy supply service provider, and classifying according to the emergency degree of the signal, wherein the signal is divided into emergency information and non-emergency information;
information transmission is carried out according to the emergency degree of the signal, a demand response request scheme is determined according to the matching pair, and power scheduling is carried out;
after the demand response is finished, historical electricity consumption data is obtained from the electricity users participating in the demand response, an electricity consumption baseline is determined, the electricity consumption data and the baseline data during the demand response are compared, the demand response effect is evaluated, and feedback is carried out;
the formation process of the demand response matching pair comprises the following steps: extracting characteristics of electricity consumption data of the power user, correspondingly generating an electricity consumption data characteristic library, and calculating matching probability of the power user and an energy service provider through the extracted characteristics; forming a demand response matching pair according to the matching probability of the power user and the energy service provider;
the specific steps of the demand response matching pair are as follows:
extracting characteristics of electricity consumption data of the power users, generating an electricity consumption data characteristic library, and calculating matching probability of the power users and energy service providers through the extracted characteristics:
the collected power consumer electricity consumption data are classified into power response times data, power response price data, power response duration data and power response load data, and four power consumer electricity consumption indexes are respectively corresponding to the four power consumer electricity consumption indexes: the method comprises the steps of performing power response times, power response prices, power response duration and power response load, and grouping classified data to obtain K groups of data;
extracting features of the obtained K groups of data to obtain K groups of feature vectors, and obtaining a power utilization data feature library;
1/n is randomly selected from all feature vectors of the electricity utilization data feature library in a random resampling mode, a sliding average value is taken as a reference feature vector of electricity utilization data of each kind of electricity utilization user, and the formula of the reference feature vector is as follows:
wherein m=1, 2, …,4 represents 4 kinds of power consumer power consumption data, i is the number of selected feature vectors under the m-th power consumer power consumption data;
classifying and identifying four kinds of power consumer power consumption data by adopting a majority voting judgment mode:
after the reference feature vectors of four kinds of power consumer electricity consumption data are obtained, the feature vectors of the commercial power data of the energy service to be judged are respectively compared with the four reference feature vectors, the data to be judged is power demand frequency data, power demand price data, power demand duration data and power demand load quantity data, the data correspond to the power response frequency data, the power response price data, the power response duration data and the power response load quantity data respectively, four absolute distances are obtained, the minimum value of the four absolute distances is obtained, the electricity consumption data corresponding to the minimum value is cast, and the commercial power data of the energy service to be judged is obtainedEach element carries out voting judgment, and the power consumption index category of the power consumer with the highest vote is used as a judgment result to be output; setting variable P toc Representing the matching probability of the power user and the energy service provider, wherein the initial value is 1, and P is changed according to the judgment result of the power utilization index of the power user toc A value; and if the matching probability is higher than the set value, forming a corresponding matching pair of the requirements.
2. The power demand response method based on the data matching algorithm as claimed in claim 1, wherein the data mining is performed by using power consumption data of power consumers and energy service providers, and the calculation process is as follows: according to the formula:
calculating a priority value Q i The method comprises the steps of carrying out a first treatment on the surface of the Wherein b 1 、b 2 All are proportional coefficients obtained according to the digging result, L i For the load variation, P i For the length of the demand response time, n is the number of power demand response times, the power consumer and the energy service provider are marked as i, i is a positive integer, and mu is a correction factor.
3. The method of claim 2, wherein the ranking the power consumer and the energy service provider comprises: high-grade power users, low-grade power users, high-grade energy service providers and low-grade energy service providers;
the power users with the priority values in the range of the high-level response threshold values are high-level power users, and the rest are low-level power users;
the energy service providers with the priority values within the range of the high-level demand threshold are high-level energy service providers, and the rest are low-level energy service providers.
4. The data matching algorithm-based power demand response method of claim 1, wherein the emergency information prioritizes notification of advanced power customers; the non-urgent information informs all power users according to the pairing principle.
5. The method of claim 1, wherein the high-level power consumer, the low-level power consumer, the high-level energy service provider, and the low-level energy service provider are re-determined when the demand response effect is evaluated and feedback is performed.
6. A computer-readable storage medium, characterized by: in which a plurality of instructions are stored, which instructions are adapted to be loaded by a processor of a terminal device and to carry out the data matching algorithm based power demand response method of any one of claims 1-5.
7. A terminal device, characterized by: comprising a processor and a computer-readable storage medium, the processor configured to implement instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and to perform the data matching algorithm based power demand response method of any one of claims 1-5.
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