CN113270897B - Intelligent power grid control system based on terminal capacity and control method thereof - Google Patents

Intelligent power grid control system based on terminal capacity and control method thereof Download PDF

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CN113270897B
CN113270897B CN202110398936.4A CN202110398936A CN113270897B CN 113270897 B CN113270897 B CN 113270897B CN 202110398936 A CN202110398936 A CN 202110398936A CN 113270897 B CN113270897 B CN 113270897B
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power generation
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intelligent load
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CN113270897A (en
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林文婷
陈果
徐冲冲
沈子翔
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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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • 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
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • 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
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/10—The dispersed energy generation being of fossil origin, e.g. diesel 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
    • 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
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/28—The renewable source being wind 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
    • 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

Abstract

The invention relates to a control method of an intelligent power grid control system based on terminal capacity, belonging to the technical field of artificial intelligence, wherein the control system comprises a shared task management module, and an intelligent load module, an intelligent load control module, an intelligent load calculation module and a power generation module which are distributed in each area; the task management module predicts the diode manufacturing task amount and the power production amount of each area based on historical task data; and the intelligent load calculation module calculates the distribution amount of the production tasks of the diodes in real time according to the current electricity prices of all areas, the power production amount predicted by the task management module and the estimated carbon emission decision value. The invention controls the power generation of the power grid and the production of the diode based on the terminal capacity, and intelligently controls and optimizes the diode production structure by taking long-term carbon emission indexes into consideration, thereby reducing the carbon emission of the diode production industry and lowering the production and operation cost of the diode.

Description

Intelligent power grid control system based on terminal capacity and control method thereof
Technical Field
The invention belongs to the field of artificial intelligence, and particularly relates to a control method of a smart grid control system based on terminal capacity.
Background
In recent years, the climate is increasingly affected by the large consumption of electric energy. Although more and more places begin to be distributed with the distributed new energy power generation system, the new energy power generation technology has larger randomness due to the close correlation with uncertain factors such as weather and climate, and further the reliable operation of a power grid is threatened after new energy is connected to the power grid. Due to the large randomness and unstable operation, the new energy power generation is not accepted and consumed by the public, and the electricity abandonment happens sometimes. Therefore, a certain amount of fossil energy is generally adopted in the prior art to generate electricity. How to ensure the optimal utilization of the installed capacity of a system while effectively controlling the influence of carbon emission on the environment is an urgent problem to be solved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a control method of a terminal capacity-based intelligent power grid control system, which can effectively control the influence of carbon emission on the environment and simultaneously ensure the optimal utilization of the installed capacity of the system.
In order to achieve the above purposes, the invention adopts the technical scheme that:
a control method of an intelligent power grid control system based on terminal capacity is used for controlling the production of light emitting diodes, the control system comprises a plurality of nuclear power generation modules distributed in a plurality of areas, a plurality of thermal power generation modules distributed in the plurality of areas, a plurality of gas power generation modules distributed in the plurality of areas, a plurality of fuel power generation modules distributed in the plurality of areas, a plurality of water conservancy power generation modules distributed in the plurality of areas and a plurality of wind power generation modules distributed in the plurality of areas, the control system also comprises a task management module, a plurality of intelligent load modules distributed in the plurality of areas, a plurality of intelligent load control modules in one-to-one correspondence with the intelligent load modules and in communication connection with the intelligent load modules, and a plurality of intelligent load calculation modules in one-to-one correspondence with the intelligent load control modules and in communication connection with the intelligent load control modules, each intelligent load module comprises a plurality of sub-load modules, and the sub-load modules cooperate to complete the diode production task of the intelligent load modules, wherein the number of the intelligent load calculation modules and the number of the intelligent load control modules are the same as the number of the intelligent load modules; the intelligent load calculation module calculates the diode production task allocation amount of the intelligent load module in real time according to the current electricity price of each region, the power production amount predicted by the task management module and the estimated carbon emission decision value;
the control system is controlled by the following steps:
s1, a task management module predicts the diode manufacturing task quantity and the power production quantity of each regional power generation module by using a long-term and short-term memory artificial neural network model based on historical task data;
s2, the intelligent load control module in the jth area carries out priority ranking on the sub-load modules based on the energy utilization efficiency corresponding to the sub-load modules, then a certain number of sub-load modules are started according to the diode manufacturing task amount predicted in the step S1, and the number of the started sub-load modules is M j ；
S3, the intelligent load calculation module in the jth region calculates the carbon emission coefficient D of the intelligent load module in the jth region based on the carbon emission indexes and the installed capacities of the power generation modules in the jth region j The calculation formula is as follows:
Figure GDA0003736385980000021
wherein, c i Carbon emission index, f, for a power generation module of known energy type i ji The installed capacity of the power generation module with the known energy type i in the j region; respectively recording the energy type of an energy generation module as 1, the energy type of a thermal power generation module as 2, the energy type of a gas power generation module as 3, the energy type of a fuel power generation module as 4, the energy type of a water conservancy power generation module as 5 and the energy type of a wind power generation module as 6;
s4, setting a power generation decision initial value X of the jth area j (0) And initial value Y of carbon emission decision j (0) Are all 0;
s5, using an iterative algorithm by an intelligent load calculation module of the jth area, starting from the 1 st time, calculating an estimated power generation amount decision value X of the jth area obtained by the k +1 st iteration by using information obtained by the last iteration j (k + 1) and the estimated carbon emission decision value Y j (k + 1), the calculation formula is respectively as follows:
Figure GDA0003736385980000031
Figure GDA0003736385980000032
wherein, PR j Is the current electricity price, X, of the j-th area j (k) Is the estimated power generation amount decision value Y of the jth area in the kth iteration j (k) The estimated carbon emission decision value of the j area in the k iteration is alpha is a known iteration step length, d is a known upper limit value of carbon emission, and P is j The predicted power production of the jth area power generation module in the step S1, wherein N is the number of areas;
s6, each intelligent load module transmits the information obtained by iteration to the intelligent load computing modules in other areas through the information transmission units in the corresponding intelligent load computing modules to perform the next iteration computation;
s7, repeating the steps S5-S6 until the difference value of the estimated power generation amount decision value and the estimated carbon emission decision value obtained by two adjacent iterative computations of each intelligent load computing module does not exceed the set threshold value, and respectively recording the final value as X j (z) and Y j (z)；
S8, determining a final value X based on the estimated power generation amount of the jth area calculated in the step S7 j (z) calculating the operation speed V of the sub-load module in the j area j (z) the calculation formula is:
Figure GDA0003736385980000033
wherein eta is j Energy efficiency of smart load module for jth region, M j The number of sub-load modules turned on for the jth zone in step S2, A j The power consumption amount when the jth region sub-load module is idle.
The invention has the beneficial technical effects that:
the invention controls the power generation of the power grid and the production of the diodes based on the installed capacity of each power generation module of the terminal, and intelligently controls and optimizes the diode production structure by taking the long-term carbon emission index into consideration, thereby reducing the carbon emission of the diode production industry, reducing the production and operation cost of the diodes, and effectively improving the resource utilization efficiency by means of task quantity prediction.
Drawings
Fig. 1 is a flowchart of a control method of a smart grid control system based on terminal capacity according to an embodiment of the present invention;
fig. 2 is a block diagram of a control system in the control method of fig. 1.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
Fig. 1 shows a flow chart of a control method of a smart grid control system based on terminal capacity according to an embodiment of the present invention, and fig. 2 shows a structural block diagram of the control system in the control method of fig. 1, and as can be seen from fig. 2, the control system includes a shared task management module, and a smart load module, a smart load control module, a smart load calculation module, and a power generation module distributed in each area;
each area is provided with a set of power generation module, the power generation module comprises a nuclear power generation module, a thermal power generation module, a gas power generation module, a fuel power generation module, a water conservancy power generation module and a wind power generation module, and the carbon emission index and the installed capacity of each power generation module are different.
And the task management module predicts the diode manufacturing task amount of the intelligent load module and the power production amount of each area by means of the long-term and short-term memory artificial neural network based on historical task data.
And the intelligent load module of each area produces the light-emitting diodes according to the distribution amount of the diode production tasks calculated by the corresponding intelligent load calculation module.
Each region is provided with an intelligent load module, an intelligent load control module and an intelligent load calculation module, each intelligent load control module corresponds to the intelligent load module one by one and is in communication connection, and each intelligent load calculation module corresponds to the intelligent load control module one by one and is in communication connection, so that the number of the intelligent load calculation modules and the number of the intelligent load control modules are the same as the number of the intelligent load modules.
Each intelligent load module comprises a plurality of sub-load modules, each intelligent load control module starts a certain number of sub-load modules according to the diode manufacturing task amount of the corresponding intelligent load module predicted by the task management module, and each sub-load module cooperatively completes the diode manufacturing task amount of the intelligent load module.
The intelligent load calculation module calculates the distribution amount of the diode production task of the intelligent load module in real time according to the carbon emission index, the installed capacity, the current electricity price, the power production amount predicted by the task management module and the estimated carbon emission decision value of each power generation module in each region, so that the system is ensured to complete the product manufacturing task and meet the carbon emission index, and the power cost is reduced.
The control system is controlled by the following steps:
s1, predicting the diode manufacturing task quantity and the power production quantity of each regional power generation module by a task management module through a neural network model based on historical task data.
Specifically, the task management module can predict the diode manufacturing task amount and the power production amount of each regional power generation module by means of the long-term and short-term memory artificial neural network.
S2, the intelligent load control module in the jth area starts a certain number of sub-load modules according to the diode manufacturing task quantity predicted in the step S1, wherein the number of the started sub-load modules is M j 。
Before the sub-load modules are started, the intelligent load control module in the jth region needs to perform priority ranking on the sub-load modules based on the energy utilization efficiency corresponding to the sub-load modules so as to ensure that the sub-load modules with high energy utilization efficiency and low carbon emission coefficient are started preferentially, so that the resource utilization efficiency is improved, and the carbon emission is reduced.
S3, the intelligent load calculation module in the jth area calculates the carbon emission system of the intelligent load module in the jth area based on the carbon emission indexes and the installed capacities of the power generation modules in the jth areaNumber D j The calculation formula is as follows:
Figure GDA0003736385980000051
wherein, c i Carbon emission index, f, for a power generation module of known energy type i ji The installed capacity of the power generation module of which the known energy type in the j-th region is i.
In this embodiment, it is noted that the energy type of the nuclear power generation module is 1, the energy type of the thermal power generation module is 2, the energy type of the gas power generation module is 3, the energy type of the fuel power generation module is 4, the energy type of the water conservancy power generation module is 5, and the energy type of the wind power generation module is 6, respectively.
S4, setting a power generation decision initial value X of the jth area j (0) Is 0, carbon emission decision initial value Y j (0) Is 0.
S5, using an iterative algorithm by an intelligent load calculation module of the jth area, starting from the 1 st time, calculating an estimated power generation amount decision value X of the jth area obtained by the (k + 1) th iteration by using information obtained by the last iteration j (k + 1) and estimated carbon emission decision value Y j (k + 1), the calculation formula is respectively as follows:
Figure GDA0003736385980000061
Figure GDA0003736385980000062
wherein, PR j Is the current electricity price, X, of the j-th area j (k) Is the estimated power generation amount decision value Y of the jth area in the kth iteration j (k) The estimated carbon emission decision value of the j area in the k iteration is alpha which is a known iteration step length, d which is a known carbon emission upper limit value, P j The power generation amount of the jth zone power generation module predicted in step S1 is N, which is the number of zones.
And S6, each intelligent load module transmits the information obtained by iteration to the intelligent load computing modules in other areas through the information transmission units in the corresponding intelligent load computing modules to perform the next iteration computation.
Here, the iteration result information mutually transmitted among the intelligent load modules of each region includes an estimated power generation amount decision value X obtained by the current iteration j (k + 1) and the estimated carbon emission decision value Y j (k+1)。
S7, repeating the steps S5-S6 until the difference value of the estimated power generation amount decision value and the estimated carbon emission decision value obtained by two adjacent iterative computations of each intelligent load computing module does not exceed the set threshold value, and respectively recording the final value as X j (z) and Y j (z), i.e. terminating the iteration when the following condition is satisfied:
|X j (k+1)-X j (k)|≤X Δ and | Y j (k+1)-Y j (k)|≤Y Δ
Wherein X Δ Setting a threshold value for the difference value of estimated power generation amount decision values obtained by calculating two adjacent iterations by the intelligent load calculation module in the jth area, wherein Y is Δ And setting a threshold value for the difference value of the estimated carbon emission decision value obtained by calculating the adjacent two iterations by the intelligent load calculation module in the jth area.
S8, determining a final value X based on the estimated power generation amount of the jth area calculated in the step S7 j (z) calculating the operation speed V of the sub-load module in the j area j (z) the calculation formula is:
Figure GDA0003736385980000071
wherein eta is j Energy efficiency of smart load module for jth region, M j The number of sub-load modules turned on for the jth zone in step S2, A j The power consumption when the sub-load module is idle.
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 are also intended to be included within the scope of the invention.

Claims (1)

1. The control method is characterized in that the control method is used for controlling the production quantity of light emitting diodes, the control system comprises a plurality of nuclear power generation modules distributed in a plurality of areas, a plurality of thermal power generation modules distributed in the plurality of areas, a plurality of gas power generation modules distributed in the plurality of areas, a plurality of fuel power generation modules distributed in the plurality of areas, a plurality of water conservancy power generation modules distributed in the plurality of areas and a plurality of wind power generation modules distributed in the plurality of areas, the control system also comprises a task management module, a plurality of intelligent load modules distributed in the plurality of areas, a plurality of intelligent load control modules in one-to-one correspondence with the intelligent load modules and in communication connection with the intelligent load modules, and a plurality of intelligent load calculation modules in one-to-one correspondence with the intelligent load control modules and in communication connection with the intelligent load control modules, each intelligent load module comprises a plurality of sub-load modules, and the sub-load modules cooperate to complete the diode production tasks of the intelligent load modules, wherein the number of the intelligent load calculation modules and the number of the intelligent load control modules are the same as the number of the intelligent load modules; the intelligent load calculation module calculates the diode production task allocation amount of the intelligent load module in real time according to the current electricity price of each region, the power production amount predicted by the task management module and the estimated carbon emission decision value;
the control system is controlled by the following steps:
s1, a task management module predicts the diode manufacturing task quantity and the power production quantity of each regional power generation module by using a long-term and short-term memory artificial neural network model based on historical task data;
s2, the intelligent load control module in the j area carries out priority on each sub-load module based on the energy utilization efficiency of each sub-load moduleRanking, and then starting a certain number of sub-load modules according to the predicted diode manufacturing task amount in the step S1, wherein the number of the started sub-load modules is M j ；
S3, the intelligent load calculation module in the jth area calculates the carbon emission coefficient D of the intelligent load module in the jth area based on the carbon emission index and the installed capacity of each power generation module in the jth area j The calculation formula is as follows:
Figure FDA0003736385970000011
wherein, c i Carbon emission index, f, for a power generation module of known energy type i ji The installed capacity of the power generation module with the known energy type i in the j region; respectively recording the energy types of a nuclear power generation module as 1, a thermal power generation module as 2, a gas power generation module as 3, a fuel power generation module as 4, a water conservancy generation module as 5 and a wind power generation module as 6;
s4, setting a power generation decision initial value X of the jth area j (0) And initial value Y of carbon emission decision j (0) Are all 0;
s5, using an iterative algorithm by an intelligent load calculation module of the jth area, starting from the 1 st time, calculating an estimated power generation amount decision value X of the jth area obtained by the (k + 1) th iteration by using information obtained by the last iteration j (k + 1) and the estimated carbon emission decision value Y j (k + 1), the calculation formula is respectively as follows:
Figure FDA0003736385970000021
Figure FDA0003736385970000022
wherein, PR j Is the current electricity price, X, of the j-th area j (k) For the k-th iterationEstimated power generation amount decision value Y of the jth region j (k) The estimated carbon emission decision value of the j area in the k iteration is alpha is a known iteration step length, d is a known upper limit value of carbon emission, and P is j The predicted power production of the jth area power generation module in the step S1, wherein N is the number of areas;
s6, each intelligent load module transmits the information obtained by iteration to the intelligent load computing modules in other areas through the information transmission units in the corresponding intelligent load computing modules to perform the next iteration computation;
s7, repeating the steps S5-S6 until the difference value of the estimated power generation amount decision value and the estimated carbon emission decision value obtained by two adjacent iterative computations of each intelligent load computing module does not exceed the set threshold value, and respectively recording the final value as X j (z) and Y j (z)；
S8, determining a final value X based on the estimated power generation amount of the jth area calculated in the step S7 j (z) calculating the operation speed V of the sub-load module in the j area j (z) the calculation formula is:
Figure FDA0003736385970000031
wherein eta is j Energy efficiency of smart load module for jth region, M j The number of sub-load modules turned on for the jth zone in step S2, A j The power consumption amount when the jth region sub-load module is idle.
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