CN112234600B - Control method of smart power grid control system based on user experience - Google Patents

Control method of smart power grid control system based on user experience Download PDF

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CN112234600B
CN112234600B CN202010904055.0A CN202010904055A CN112234600B CN 112234600 B CN112234600 B CN 112234600B CN 202010904055 A CN202010904055 A CN 202010904055A CN 112234600 B CN112234600 B CN 112234600B
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林文婷
陈果
徐冲冲
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Central South University
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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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
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    • G06Q50/06—Energy 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
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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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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
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    • 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
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Abstract

The invention relates to a control method of a smart grid control system based on user experience, which is based on a user experience model, recognizes and controls the operation mode of a user side module through a smart meter, improves the user experience and greatly increases the consumption of renewable energy sources, thereby avoiding the impact of renewable energy source power generation on a smart grid.

Description

Control method of smart power grid control system based on user experience
Technical Field
The invention belongs to the field of artificial intelligence, and particularly relates to a control method of an intelligent power grid control system based on user experience.
Background
In recent years, with the rapid development of new energy power generation technology, the application of the new energy power generation technology is distributed to every household. Although more and more places begin to arrange the distributed new energy power generation system, the new energy power generation technology has larger randomness due to close correlation with uncertain factors such as weather and climate, and further the new energy is threatened to the reliable operation of a power grid after being connected with the power grid; due to the fact that randomness is high, operation is unstable, new energy power generation is not accepted and consumed by the public, and power abandon sometimes occurs. In addition, many devices in home users have an intelligent trend, and how to reasonably control the intelligent devices to reduce the impact of peak power utilization on the power grid is also a problem worthy of research.
Therefore, the technical problems that the electric quantity generated by new energy is difficult to accept and consume by the public, the control of intelligent equipment at the user side still cannot be kept consistent with the power generation condition of a power grid and the like exist in the prior art.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a control method of an intelligent power grid control system based on user experience, so as to realize the consumption of renewable energy and reduce the impact of peak power consumption on a power grid.
In order to achieve the purpose, the invention adopts the technical scheme that:
a control method of a smart grid control system based on user experience comprises a plurality of renewable energy power generation modules, a plurality of energy storage modules, a plurality of smart meter control modules, a plurality of load control modules, a plurality of new energy control modules and a plurality of smart devices; each renewable energy power generation module, each energy storage module and each intelligent device are connected through power transmission lines, and electric energy can be transmitted among the renewable energy power generation modules and the energy storage modules; the intelligent instrument control modules, the new energy control modules, the load control modules and the intelligent equipment are in communication connection and can transmit control information instructions to each other;
the control system is controlled by the following steps:
1) the intelligent instrument control module sends instructions to each new energy control module to request the new energy control module to send new energy electricity selling price information, predicted generating capacity information and generating uncertainty parameter information;
2) each new energy power generation module calculates new energy power selling price information based on capital construction cost, operation maintenance cost, boundary cost of renewable energy power generation and return on investment rate, calculates power generation capacity information and power generation uncertainty parameter information based on historical information and weather information, and sends the information to the intelligent instrument control module;
3) the intelligent instrument control module sends an instruction to the load control module to request the load control module to send information about whether to participate in experience degree adjustment, operation parameters and adjustable capacity;
4) the load control module receives the instruction, determines whether to participate in experience degree control in the current time period, and sends information to the intelligent instrument control module; if the load module participates in the operation, executing according to the step 5), and if the load module does not participate in the operation, operating according to a user setting mode;
5) the intelligent instrument control module receives the information of each load control module and determines the number M of intelligent equipment participating in experience degree adjustment;
6) for intelligent equipment i participating in experience degree adjustment, i belongs to 1,2, … and M, calculating an experience degree index of a user i in a time period t based on running start time and running end time set by the user;
7) numbering the intelligent equipment from high to low according to the user experience index in the step 6) to be 1,2, …, M, and bringing the intelligent equipment into a list to be controlled;
8) selecting a new energy power generation module with the lowest current price information and intelligent equipment participating in experience degree adjustment and having the largest experience degree index from a current list to be controlled, and calculating the operation parameters f one by one based on the following formula and a numerical solving tooli tThe value:
Figure BDA0002660763990000021
wherein
Figure BDA0002660763990000022
Figure BDA0002660763990000023
For the power consumption, p, of the intelligent device i during the time period ttThe price of the electricity is the renewable energy source,
and Y isi tTo satisfy the set of conditions:
Figure BDA0002660763990000031
Figure BDA0002660763990000032
the value is 0 or 1, wherein 0 represents that the operation parameter of the intelligent device i in the time period t is not determined through calculation, and 1 represents that the operation parameter of the intelligent device i in the time period t is determined through calculation;
fi tis 0 or 1,0 represents that the intelligent device i does not operate in the time period t, 1 represents that the intelligent device i operates in the time period t, theta is a weight parameter, and theta is equal to [0,1 ]]Beta is a constant greater than zero, set according to the characteristics of the user, riThe number of time periods required for the intelligent device to operate,
Figure BDA0002660763990000033
an experience index of the user i in the time period t;
9) obtaining an operating parameter value fi tThen, deleting the intelligent equipment i participating in the control from the list to be controlled, and updating the corresponding intelligent equipment i
Figure BDA0002660763990000034
And returning to the step 7) until the operation parameter values of all the intelligent devices are obtained;
10) if the obtained intelligent equipment operation parameter f is calculatedi tIf the values are 0, the renewable energy generating capacity in the current time period is smaller than the sum of the electric quantity required by the load, at the moment, the energy storage modules are started one by one, and the user load requirements are matched according to the sequence of the experience indexes from high to low until the electric quantity of the energy storage modules is used up;
11) and controlling the intelligent equipment to operate in each time period according to the calculated operation parameter values.
Further, in step 6), based on the operation start time and the operation end time set by the user, the experience index of the user i in the time period t is respectively calculated according to the following formula:
Figure BDA0002660763990000035
wherein B isiSmart device on-time set for user, EiAnd alpha is a constant larger than 0 for the intelligent equipment closing time set by the user.
The invention has the beneficial technical effects that:
according to the control system, each new energy power generation module calculates new energy power selling price information based on capital construction cost, operation maintenance cost, boundary cost of renewable energy power generation and investment return rate, calculates power generation amount information and power generation uncertainty parameter information based on historical information and weather information, and combines the adjustable performance of load, so that the consumption of renewable energy is realized, and the impact of peak power consumption on a power grid is reduced. Through the control method of adjusting based on the user experience, the matching of the user satisfaction and the new energy power generation can be realized, and the power consumption experience problem of the user is fully considered while the new energy power generation is consumed.
Drawings
Fig. 1 is a flow chart of a control method of a smart grid control system based on user experience provided by the present invention.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
The invention provides a control method of an intelligent power grid control system based on user experience, wherein the control system comprises a plurality of renewable energy power generation modules, a plurality of energy storage modules, a plurality of intelligent instrument control modules, a plurality of load control modules, a plurality of new energy control modules and a plurality of intelligent devices; each renewable energy power generation module, each energy storage module and each intelligent device are connected through power transmission lines, and electric energy can be transmitted among the renewable energy power generation modules and the energy storage modules; the intelligent instrument control modules, the new energy control modules, the load control modules and the intelligent equipment are in communication connection and can transmit control information instructions to each other;
as shown in fig. 1, the control system performs control by:
step 1: the intelligent instrument control module sends instructions to each new energy control module to request the new energy control module to send new energy electricity selling price information, predicted generating capacity information and generating uncertainty parameter information;
step 2: each new energy power generation module calculates new energy power selling price information based on capital construction cost, operation maintenance cost, boundary cost of renewable energy power generation and return on investment rate, calculates power generation capacity information and power generation uncertainty parameter information based on historical information and weather information, and sends the information to the intelligent instrument control module;
and step 3: the intelligent instrument control module sends an instruction to the load control module to request the load control module to send information about whether to participate in experience degree adjustment, operation parameters and adjustable capacity;
and 4, step 4: the load control module receives the instruction, determines whether to participate in experience degree control in the current time period, and sends information to the intelligent instrument control module; if the load module participates in the operation, executing according to the step 5), and if the load module does not participate in the operation, operating according to a user setting mode;
and 5: the intelligent instrument control module receives the information of each load control module and determines the number M of intelligent equipment participating in experience degree adjustment;
step 6: for intelligent equipment i participating in experience degree adjustment, i belongs to 1,2, …, M, based on the running start time and the running end time set by the user, respectively calculating the experience degree index of the user i in the time period t according to the following formula:
Figure BDA0002660763990000051
wherein B isiSmart device on-time set for user, EiAnd alpha is a constant larger than 0 for the intelligent equipment closing time set by the user.
And 7: numbering the intelligent equipment from high to low according to the user experience index in the step 6, wherein the intelligent equipment is numbered as 1,2, …, M and is included in the list to be controlled;
and 8: selecting a new energy power generation module with the lowest current price information and intelligent equipment participating in experience degree adjustment and having the largest experience degree index from a current list to be controlled, and calculating the operation parameters f one by one based on the following formula and a numerical solving tooli tThe value:
Figure BDA0002660763990000052
wherein
Figure BDA0002660763990000053
Figure BDA0002660763990000054
For the power consumption, p, of the intelligent device i during the time period ttFor electricity price of renewable energy
And Y isi tTo satisfy the set of conditions:
Figure BDA0002660763990000055
Figure BDA0002660763990000056
the value is 0 or 1, wherein 0 represents that the operation parameter of the intelligent device i in the time period t is not determined through calculation, and 1 represents that the operation parameter of the intelligent device i in the time period t is determined through calculation;
fi tis 0 or 1,0 represents that the intelligent device i does not operate in the time period t, 1 represents that the intelligent device i operates in the time period t, beta is a constant larger than zero and is set according to the characteristics of the user, theta is a weight parameter, and theta is within the range of 0 and 1]，riThe number of time periods required for the intelligent device to operate,
Figure BDA0002660763990000057
an experience index of the user i in the time period t;
step 9, obtaining an operation parameter value fi tThen, deleting the intelligent equipment i participating in the control from the list to be controlled, and updating the corresponding intelligent equipment i
Figure BDA0002660763990000061
And 7, returning to the step 7 until the operation parameter values of all the intelligent devices are obtained;
step 10: if the obtained intelligent equipment operation parameter f is calculatedi tIf the values are 0, the renewable energy generating capacity in the current time period is smaller than the sum of the electric quantity required by the load, at the moment, the energy storage modules are started one by one, and the user load requirements are matched according to the sequence of the experience indexes from high to low until the electric quantity of the energy storage modules is used up;
step 11: and controlling the intelligent equipment to operate in each time period according to the calculated operation parameter values.
The above-described embodiments are merely illustrative of the present invention, which may be embodied in other specific forms or in other specific forms without departing from the spirit or essential characteristics thereof. The described embodiments are, therefore, to be considered in all respects as illustrative and not restrictive. The scope of the invention should be indicated by the appended claims, and any changes that are equivalent to the intent and scope of the claims should be construed to be included therein.

Claims (2)

1. A control method of a smart grid control system based on user experience is characterized in that,
the control system comprises a plurality of renewable energy power generation modules, a plurality of energy storage modules, a plurality of intelligent instrument control modules, a plurality of load control modules, a plurality of new energy control modules and a plurality of intelligent devices; each renewable energy power generation module, each energy storage module and each intelligent device are connected through power transmission lines, and electric energy can be transmitted among the renewable energy power generation modules and the energy storage modules; the intelligent instrument control modules, the new energy control modules, the load control modules and the intelligent equipment are in communication connection and can transmit control information instructions to each other;
the control system is controlled by the following steps:
1) the intelligent instrument control module sends instructions to each new energy control module to request the new energy control module to send new energy electricity selling price information, predicted generating capacity information and generating uncertainty parameter information;
2) each new energy power generation module calculates new energy power selling price information based on capital construction cost, operation maintenance cost, boundary cost of renewable energy power generation and return on investment rate, calculates power generation capacity information and power generation uncertainty parameter information based on historical information and weather information, and sends the information to the intelligent instrument control module;
3) the intelligent instrument control module sends an instruction to the load control module to request the load control module to send information about whether to participate in experience degree adjustment, operation parameters and adjustable capacity;
4) the load control module receives the instruction, determines whether to participate in experience degree control in the current time period, and sends information to the intelligent instrument control module; if the load module participates in the operation, executing according to the step 5), and if the load module does not participate in the operation, operating according to a user setting mode;
5) the intelligent instrument control module receives the information of each load control module and determines the number M of intelligent equipment participating in experience degree adjustment;
6) for intelligent equipment i participating in experience degree adjustment, i belongs to 1,2, … and M, calculating an experience degree index of a user i in a time period t based on running start time and running end time set by the user;
7) numbering the intelligent equipment from high to low according to the user experience index in the step 6) to be 1,2, …, M, and bringing the intelligent equipment into a list to be controlled;
8) selecting a new energy power generation module with the lowest current price information and intelligent equipment participating in experience degree adjustment and having the largest experience degree index from a current list to be controlled, and calculating the operation parameters f one by one based on the following formula and a numerical solving tooli tThe value:
Figure FDA0002660763980000011
wherein
Figure FDA0002660763980000021
Figure FDA0002660763980000022
For the power consumption, p, of the intelligent device i during the time period ttThe price of the electricity is the renewable energy source,
and Y isi tTo satisfy the set of conditions:
Figure FDA0002660763980000023
Figure FDA0002660763980000024
the value is 0 or 1, wherein 0 represents that the operation parameter of the intelligent device i in the time period t is not determined through calculation, and 1 represents that the operation parameter of the intelligent device i in the time period t is determined through calculation;
fi tis 0 or 1,0 represents that the intelligent device i does not operate in the time period t, 1 represents that the intelligent device i operates in the time period t, theta is a weight parameter, and theta is equal to [0,1 ]]Beta is a constant greater than zero, set according to the characteristics of the user, riThe number of time periods required for the intelligent device to operate,
Figure FDA0002660763980000025
an experience index of the user i in the time period t;
9) obtaining an operating parameter value fi tThen, deleting the intelligent equipment i participating in the control from the list to be controlled, and updating the corresponding intelligent equipment i
Figure FDA0002660763980000026
And returning to the step 7) until the operation parameter values of all the intelligent devices are obtained;
10) if the calculated operation parameter fi tIf the values are 0, the renewable energy generating capacity in the current time period is smaller than the sum of the electric quantity required by the load, at the moment, the energy storage modules are started one by one, and the user load requirements are matched according to the sequence of the experience indexes from high to low until the electric quantity of the energy storage modules is used up;
11) and controlling the intelligent equipment to operate in each time period according to the calculated operation parameter values.
2. The control method of the smart grid control system based on the user experience according to claim 1, wherein in step 6), based on the operation start time and the operation end time set by the user, the experience index of the user i in the time period t is respectively calculated according to the following formula:
Figure FDA0002660763980000027
wherein B isiSmart device on-time set for user, EiSet for the user the turn-off time of the smart device, α isA constant greater than 0.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009052450A2 (en) * 2007-10-17 2009-04-23 V2Green, Inc. Business methods in a power aggregation system for distributed electric resources
CN107147115A (en) * 2017-06-16 2017-09-08 北京邮电大学 A kind of user side energy management method based on multiple time scale model
CN110707691A (en) * 2019-10-14 2020-01-17 华中科技大学 Control method of distributed intelligent power grid monitoring system based on users
CN110929925A (en) * 2019-11-15 2020-03-27 国家电网有限公司 Electric vehicle charging station planning method based on environment temperature and user behaviors

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180198277A1 (en) * 2017-01-12 2018-07-12 Damian Antone Bollermann Methods And Systems For A Renewable Electricity System
US10554046B2 (en) * 2017-12-18 2020-02-04 International Business Machines Corporation Virtualization of large-scale energy storage

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009052450A2 (en) * 2007-10-17 2009-04-23 V2Green, Inc. Business methods in a power aggregation system for distributed electric resources
CN107147115A (en) * 2017-06-16 2017-09-08 北京邮电大学 A kind of user side energy management method based on multiple time scale model
CN110707691A (en) * 2019-10-14 2020-01-17 华中科技大学 Control method of distributed intelligent power grid monitoring system based on users
CN110929925A (en) * 2019-11-15 2020-03-27 国家电网有限公司 Electric vehicle charging station planning method based on environment temperature and user behaviors

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Temporal Pattern Attention-Based Sequence to Sequence model for Multistep Individual Load Forecasting;Chongchong Xu;《IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society》;20200821;全文 *
一种基于实时电价的HEMS家电最优调度方法;刘经浩等;《计算机应用研究》;20140827(第01期);全文 *

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