CN112398124B - Optimization control method, device and equipment for regional micro-grid and readable storage medium - Google Patents

Optimization control method, device and equipment for regional micro-grid and readable storage medium Download PDF

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CN112398124B
CN112398124B CN202011247095.9A CN202011247095A CN112398124B CN 112398124 B CN112398124 B CN 112398124B CN 202011247095 A CN202011247095 A CN 202011247095A CN 112398124 B CN112398124 B CN 112398124B
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optimization control
power
control model
time
micro
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CN112398124A (en
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周炳华
姚海燕
吴金荣
崔金栋
余桂华
李锋
王丽芳
徐辉
张哲斌
向锋铭
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Yuhang Branch Of Hangzhou Electric Power Design Institute Co ltd
Northeast Electric Power University
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Yuhang Branch Of Hangzhou Electric Power Design Institute Co ltd
Northeast Dianli University
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • 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
    • H02J13/00001—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 characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • 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
    • H02J13/00006—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 characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • 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
    • H02J13/00006—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 characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • H02J13/00016—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 characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using a wired telecommunication network or a data transmission bus
    • 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
    • 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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • 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
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • 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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    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02B90/20—Smart grids as enabling technology in buildings 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
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Abstract

The invention discloses an optimization control method of a regional microgrid, which comprises the steps of dividing a target regional microgrid into a main power distribution network layer, a micro-energy source network layer and a user layer, respectively establishing corresponding optimization control models for the main power distribution network layer, the micro-energy source network layer and the user layer, then comprehensively solving each optimization control model to obtain optimization control parameters of the target regional microgrid, and realizing comprehensive optimization of the comprehensive production cost and the energy consumption cost of the regional microgrid through multi-objective optimization planning; compared with the traditional optimization planning scheme of the power distribution network, the operation of the regional micro-grid is controlled based on the optimization control parameters, and the efficient and reasonable operation of the regional micro-grid is realized. The invention also discloses an optimization control device, equipment and a readable storage medium of the regional micro-grid, which have the beneficial effects.

Description

Optimization control method, device and equipment for regional micro-grid and readable storage medium
Technical Field
The invention relates to the technical field of electric power, in particular to an optimal control method, device and equipment for a regional micro-grid and a readable storage medium.
Background
The problems of energy crisis, environmental pollution and the like make the traditional energy development mode based on fossil energy difficult to continue, and the energy production mode and the consumption concept need to be updated urgently to ensure the efficient development and sustainable supply of energy. Under the background, a new energy system, namely an energy internet concept, which realizes multi-energy complementation and multi-network coupling, supports the access of a large amount of distributed energy and the wide participation of users, and deeply fuses information technologies is provided. The energy Internet provides a new technical means for improving the comprehensive energy utilization efficiency and comprehensively adjusting the energy production and consumption modes. However, due to the current technical barriers and the sealing performance of the conventional energy industry, it is difficult to realize the actual operation of the large-scale energy internet in a short period of time. Therefore, the micro energy network is used as a miniature of the energy Internet, and the operation optimization and energy management research of the micro energy network have important guiding significance on the development of the energy Internet.
However, the algorithm of the optimization planning of the traditional power distribution network is single, the optimization planning is difficult to adapt to the appearance of various micro-grids and the multi-grid coupling operation planning, the comprehensive optimization of comprehensive production cost and energy consumption cost cannot be achieved, and the high-efficiency reasonable operation of the regional micro-grid is not facilitated.
Disclosure of Invention
The invention aims to provide an optimal control method, device and equipment for a regional micro-grid and a readable storage medium, which are used for realizing efficient and reasonable operation of the regional micro-grid through multi-objective optimal planning.
In order to solve the technical problem, the invention provides an optimal control method for a regional microgrid, which comprises the following steps:
dividing a target area microgrid into a main power distribution network layer, a micro-energy source network layer and a user layer, and respectively establishing corresponding optimized control models for the main power distribution network layer, the micro-energy source network layer and the user layer;
comprehensively solving each optimized control model to obtain optimized control parameters of the micro-grid in the target area;
and controlling the operation of the micro-grid of the target area based on the optimized control parameters.
Optionally, the respectively establishing corresponding optimal control models for the main power distribution network layer, the micro energy network layer, and the user layer specifically includes:
establishing a first optimization control model aiming at maximizing the income of a power supply company for the main power distribution network layer;
establishing a second optimized control model aiming at minimizing energy consumption for the micro energy resource network layer;
establishing a third optimization control model aiming at minimizing user energy expenditure for the user layer;
correspondingly, the comprehensive solution of each optimal control model to obtain the optimal control parameters of the target area microgrid specifically comprises:
inputting the initial unit state parameters of the micro-grid in the target area into the third optimization control model, and then solving the third optimization control model to obtain third optimization control parameters;
after the third optimization control parameter is input into the second optimization control model, solving the second optimization control model to obtain a second optimization control parameter;
and after the second optimized control parameter is input into the first optimized control model, solving the first optimized control model to obtain a first optimized control parameter.
Optionally, the objective function of the first optimization control model is specifically represented by the following formula:
Figure 100002_DEST_PATH_IMAGE001
；
the constraint condition of the first optimization control model is specifically represented by the following formula:
Figure DEST_PATH_IMAGE002
；
Figure 100002_DEST_PATH_IMAGE003
；
Figure DEST_PATH_IMAGE004
；
the objective function of the second optimization control model is specifically represented by the following formula:
Figure 100002_DEST_PATH_IMAGE005
；
the objective function of the third optimization control model is specifically represented by the following formula:
Figure DEST_PATH_IMAGE006
；
wherein,
Figure 100002_DEST_PATH_IMAGE007
in order to be a benefit of the power supply company,
Figure DEST_PATH_IMAGE008
is composed oftThe unit electricity rate of the time period,
Figure 100002_DEST_PATH_IMAGE009
is composed oftThe generated power of the time period is,
Figure DEST_PATH_IMAGE010
is composed oftThe power generation cost of the generator set of the target area microgrid,
Figure DEST_PATH_IMAGE011
is composed oft(ii) emissions cost of the generator set over time;
Figure DEST_PATH_IMAGE012
for the amount of electricity sold by the power supply company,
Figure 100002_DEST_PATH_IMAGE013
is composed oftThe photovoltaic power generation is carried out in a time period,
Figure DEST_PATH_IMAGE014
is composed oftThe fan generates electricity and outputs power in a time period,
Figure DEST_PATH_IMAGE015
is composed oftThe internal combustion engine generates power in a time interval,
Figure DEST_PATH_IMAGE016
is composed oftThe discharge state value of the storage battery in the time period,
Figure 100002_DEST_PATH_IMAGE017
is composed oftThe period of time is the discharge power of the battery,
Figure DEST_PATH_IMAGE018
is composed oftThe actual electrical load of the user over the period of time,
Figure 100002_DEST_PATH_IMAGE019
is composed oftThe time interval is the power consumption of the refrigerating machine,
Figure DEST_PATH_IMAGE020
is composed oftThe ice-making state value of the time period,
Figure 100002_DEST_PATH_IMAGE021
is composed oftThe electric power consumed by the ice making of the time interval ice storage tank,
Figure DEST_PATH_IMAGE022
is composed oftThe ice-melt state value for a time period,
Figure DEST_PATH_IMAGE023
is composed oftThe power consumption of the ice melting in the time interval,
Figure DEST_PATH_IMAGE024
is composed oftThe time period is a value of a state of charge of the battery,
Figure DEST_PATH_IMAGE025
is composed oftTime interval of the charging power of the storage battery;
Figure DEST_PATH_IMAGE026
is composed oftThe heat-generating power of the time-interval heat exchanger,
Figure 100002_DEST_PATH_IMAGE027
is composed oftThe heat-generating power of the direct-fired engine in a time period,
Figure DEST_PATH_IMAGE028
is composed oftThe heat release state value of the time-zone heat storage device,
Figure DEST_PATH_IMAGE029
is composed oftThe period of time is the heat-releasing power of the thermal storage device,
Figure DEST_PATH_IMAGE030
is composed oftThe actual heat load of the user over the period of time,
Figure 100002_DEST_PATH_IMAGE031
is composed oftThe heat storage state value of the heat storage device is set for a period of time,
Figure DEST_PATH_IMAGE032
is composed oftThe heat storage power of the heat storage device is set in time interval;
Figure 100002_DEST_PATH_IMAGE033
is composed oftThe cooling power of the direct-fired engine is divided into a plurality of time periods,
Figure DEST_PATH_IMAGE034
is composed oftThe refrigerating power of the refrigerator is set for a period of time,
Figure DEST_PATH_IMAGE035
is composed oftThe ice-melting cooling power of the time period,
Figure DEST_PATH_IMAGE036
is composed oftActual cooling load of the user over time;
Figure 100002_DEST_PATH_IMAGE037
for the energy consumption of the micro-energy net layer,
Figure DEST_PATH_IMAGE038
is a cost factor for the output of the internal combustion engine,
Figure 100002_DEST_PATH_IMAGE039
is the cost coefficient of the direct combustion engine output,
Figure DEST_PATH_IMAGE040
is composed oftThe power transmitted to the cold and hot energy buses by the direct-fired machine in a time period,
Figure 100002_DEST_PATH_IMAGE041
a cost penalty function factor for the rejection of light,
Figure DEST_PATH_IMAGE042
the output is predicted for the photovoltaic system,
Figure DEST_PATH_IMAGE043
a cost penalty function factor for the wind curtailment,
Figure DEST_PATH_IMAGE044
predicting the output for the wind energy;
Figure DEST_PATH_IMAGE045
the energy consumption is paid for the user,
Figure DEST_PATH_IMAGE046
as a processing cost factor of the internal combustion engine,
Figure 100002_DEST_PATH_IMAGE047
is the output cost coefficient of the direct-fired engine,
Figure DEST_PATH_IMAGE048
in order to maintain the coefficients for the thermal energy storage,
Figure 100002_DEST_PATH_IMAGE049
for electric refrigerationThe cost factor of the output force is,
Figure DEST_PATH_IMAGE050
is a cost coefficient for processing the ice storage battery.
Optionally, the comprehensive solution of each optimal control model is used to obtain optimal control parameters of the target area microgrid, and specifically includes:
and respectively solving each optimized control model by using a genetic algorithm.
Optionally, the respectively solving of each optimization control model by using a genetic algorithm specifically includes:
and respectively solving each optimized control model by using a genetic algorithm, and screening individuals by using a simulated annealing algorithm in each iterative operation of each optimized control model.
Optionally, screening the individuals by using a simulated annealing algorithm, specifically, calculating the probability of the selected individuals by using the following formula:
Figure 100002_DEST_PATH_IMAGE051
；
wherein,
Figure DEST_PATH_IMAGE052
the probability of being selected for an individual is,
Figure 100002_DEST_PATH_IMAGE053
for the fitness value corresponding to the current decision variable,
Figure DEST_PATH_IMAGE054
to generate a fitness value for the new decision variable,
Figure 100002_DEST_PATH_IMAGE055
is a constant of boltzmann's constant,
Figure DEST_PATH_IMAGE056
is the annealing temperature.
Optionally, screening the individuals by using a simulated annealing algorithm, specifically, calculating the probability of the selected individuals by using the following formula:
Figure 100002_DEST_PATH_IMAGE057
；
wherein,
Figure 958420DEST_PATH_IMAGE052
the probability of being selected for an individual is,
Figure 607707DEST_PATH_IMAGE053
to generate a fitness value for the new decision variable,
Figure DEST_PATH_IMAGE058
is the boltzmann constant, and is,
Figure 100002_DEST_PATH_IMAGE059
is the annealing temperature.
In order to solve the above technical problem, the present invention further provides an optimization control device for a local microgrid, including:
the modeling unit is used for dividing a target area microgrid into a main power distribution network layer, a micro-energy source network layer and a user layer and respectively establishing corresponding optimized control models for the main power distribution network layer, the micro-energy source network layer and the user layer;
the solving unit is used for comprehensively solving each optimized control model to obtain optimized control parameters of the micro-grid in the target area;
and the control unit is used for controlling the operation of the micro-grid of the target area based on the optimized control parameters.
In order to solve the above technical problem, the present invention further provides an optimization control device for a local microgrid, including:
a memory for storing instructions, wherein the instructions comprise the steps of any one of the above methods for optimizing and controlling the regional microgrid;
a processor to execute the instructions.
In order to solve the above technical problem, the present invention further provides a readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method for optimizing and controlling a regional microgrid according to any one of the above items.
The optimization control method of the regional microgrid provided by the invention comprises the steps of firstly dividing a target regional microgrid into a main power distribution network layer, a micro-energy source network layer and a user layer, respectively establishing corresponding optimization control models for the main power distribution network layer, the micro-energy source network layer and the user layer, then comprehensively solving each optimization control model to obtain optimization control parameters of the target regional microgrid, and realizing comprehensive optimization of the comprehensive production cost and the energy consumption cost of the regional microgrid through multi-objective optimization planning; compared with the traditional optimization planning scheme of the power distribution network, the operation of the regional micro-grid is controlled based on the optimization control parameters, and the efficient and reasonable operation of the regional micro-grid is realized.
The invention also provides an optimization control device, equipment and a readable storage medium of the regional micro-grid, which have the beneficial effects and are not described again.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a flowchart of an optimization control method for a local microgrid provided in an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a principle of layering a regional microgrid provided in an embodiment of the present invention;
FIG. 3 is a diagram of a micro energy grid model according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an optimization control apparatus for a local microgrid according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an optimization control device of a local microgrid provided in an embodiment of the present invention.
Detailed Description
The core of the invention is to provide an optimal control method, device and equipment of a regional micro-grid and a readable storage medium, which are used for realizing the efficient and reasonable operation of the regional micro-grid through multi-objective optimal planning.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of an optimization control method for a local microgrid provided in an embodiment of the present invention; fig. 2 is a schematic diagram illustrating a principle of layering a regional microgrid provided in an embodiment of the present invention; fig. 3 is a diagram of a micro energy grid model according to an embodiment of the present invention.
As shown in fig. 1, the method for optimizing and controlling a local microgrid provided by an embodiment of the present invention includes:
s101: the method comprises the steps of dividing a target area micro-grid into a main power distribution network layer, a micro-energy network layer and a user layer, and respectively establishing corresponding optimized control models for the main power distribution network layer, the micro-energy network layer and the user layer.
S102: and comprehensively solving each optimized control model to obtain optimized control parameters of the microgrid of the target area.
S103: and controlling the operation of the micro-grid of the target area based on the optimized control parameters.
In a specific implementation, a target area microgrid is divided into a main power distribution grid layer, a micro-energy grid layer and a user layer, and an area microgrid operation scheme which enables expenditure of each layer to be minimum or income to be maximum is sought. As shown in fig. 2, in the target area microgrid, the main power distribution network serves as a leader of the microgrid, and the microgrid serves as a leader of the microgrid at this stage; in the micro energy source network layer, the micro energy source network is used as a leader of the user layer, and the electricity users are in a leading position in the micro power network of the target area.
In step S101, corresponding optimal control models are respectively established for the main power distribution network layer, the micro-energy network layer, and the user layer, which may specifically include:
establishing a first optimization control model aiming at maximizing the income of a power supply company for a main power distribution network layer;
establishing a second optimization control model aiming at minimizing energy consumption for the micro-energy source network layer;
establishing a third optimization control model aiming at minimizing the user energy expenditure for the user layer;
correspondingly, in step S102, each optimal control model is solved comprehensively to obtain optimal control parameters of the microgrid of the target area, which specifically includes:
inputting the initial unit state parameters of the micro-grid in the target area into a third optimization control model, and then solving the third optimization control model to obtain third optimization control parameters;
after the third optimization control parameter is input into the second optimization control model, the second optimization control model is solved to obtain a second optimization control parameter;
and after the second optimization control parameter is input into the first optimization control model, solving the first optimization control model to obtain the first optimization control parameter.
In view of this, embodiments of the present invention provide specific descriptions of a first optimization control model, a second optimization control model, and a third optimization control model.
As shown in fig. 3, the target micro energy grid may be divided into an electric power bus, a heat energy bus, and a cold energy bus, and the main distribution grid, the distributed internal combustion engine, the storage battery, the fan, and the photovoltaic power generation unit are connected to the electric power bus to supply electric power to the electric power bus or use electric power. The ice storage battery and the electric refrigerator output cold energy to the cold energy bus for cold load use. The internal combustion engine conveys heat energy to a heat energy bus for heating load through the heat exchanger and the cooling and heating machine.
Based on the model shown in fig. 3, the objective function of the first optimization control model can be specifically expressed by the following formula, aiming at maximizing the revenue of the power supply company at the main power distribution network layer:
Figure 66370DEST_PATH_IMAGE001
（1）
wherein,
Figure DEST_PATH_IMAGE060
in order to be profitable for the power supply company,
Figure 743909DEST_PATH_IMAGE008
is composed oftThe unit electricity rate of the time period,
Figure 920812DEST_PATH_IMAGE009
is composed oftThe generated power of the time period is,
Figure 436107DEST_PATH_IMAGE010
is composed oftThe power generation cost of the generator set of the micro-grid in the target area of the time period,
Figure 100002_DEST_PATH_IMAGE061
is composed oftEmission cost of the generator set in time period.
The constraints of the three loads of electricity, heat and cold are comprehensively considered in the micro-energy grid layer, and the constraint conditions of the second optimal control model are specifically represented by the following formula:
the balance equation for the power bus may be defined as:
Figure DEST_PATH_IMAGE062
（2）
wherein,
Figure 100002_DEST_PATH_IMAGE063
in order to supply the power of the power company,
Figure 31168DEST_PATH_IMAGE013
is composed oftThe photovoltaic power generation is carried out in a time period,
Figure DEST_PATH_IMAGE064
is composed oftThe fan generates electricity and outputs power in a time period,
Figure 809768DEST_PATH_IMAGE015
is composed oftThe internal combustion engine generates power in a time interval,
Figure 224700DEST_PATH_IMAGE016
is composed oftThe discharge state value of the storage battery in the time period,
Figure 543686DEST_PATH_IMAGE017
is composed oftThe discharge power of the storage battery is used for a period of time,
Figure 852307DEST_PATH_IMAGE018
is composed oftThe actual electrical load of the user over the period of time,
Figure 660863DEST_PATH_IMAGE019
is composed oftThe time interval is the power consumption of the refrigerating machine,
Figure 422146DEST_PATH_IMAGE020
is composed oftThe ice-making state value of the time period,
Figure 544823DEST_PATH_IMAGE021
is composed oftThe electric power consumed by the ice making of the time interval ice storage tank,
Figure 100002_DEST_PATH_IMAGE065
is composed oftThe value of the ice-melt state for a time period,
Figure DEST_PATH_IMAGE066
is composed oftThe power consumption of the ice melting in the time interval,
Figure 100002_DEST_PATH_IMAGE067
is composed oftThe state of charge value of the time-of-day battery,
Figure DEST_PATH_IMAGE068
is composed oftCharging power of the battery is timed.
The thermal busbar balance equation can be defined as:
Figure 100002_DEST_PATH_IMAGE069
（3）
wherein,
Figure DEST_PATH_IMAGE070
is composed oftThe heat-generating power of the time-interval heat exchanger,
Figure DEST_PATH_IMAGE071
is composed oftThe heat-generating power of the direct-fired engine in a time period,
Figure DEST_PATH_IMAGE072
is composed oftThe heat release state value of the time-period heat storage device,
Figure 100002_DEST_PATH_IMAGE073
is composed oftThe heat-releasing power of the time-interval heat storage device,
Figure DEST_PATH_IMAGE074
is composed oftThe actual heat load of the user over the period of time,
Figure 986912DEST_PATH_IMAGE031
is composed oftThe heat storage state value of the time-period heat storage device,
Figure 717102DEST_PATH_IMAGE032
is composed oftThe heat storage power of the time interval heat storage device.
The equilibrium equation for the cold bus can be defined as:
Figure 100002_DEST_PATH_IMAGE075
（4）
wherein,
Figure 824735DEST_PATH_IMAGE033
is composed oftTime-interval direct combustion engineThe cold power is used for cooling the air,
Figure 485523DEST_PATH_IMAGE034
is composed oftThe refrigerating power of the refrigerator is increased in a time period,
Figure 237579DEST_PATH_IMAGE035
is composed oftThe ice-melting cooling power of the time period,
Figure DEST_PATH_IMAGE076
is composed oftThe actual cooling load of the user during the time period.
In the micro-grid layer, the objective function of the second optimization control model is specifically expressed by the following formula:
Figure 935408DEST_PATH_IMAGE005
（5）
wherein,
Figure 100002_DEST_PATH_IMAGE077
the energy consumption of the micro-energy network layer,
Figure DEST_PATH_IMAGE078
is a cost factor for the output of the internal combustion engine,
Figure 264758DEST_PATH_IMAGE039
for the cost factor of the direct combustion engine output,
Figure 666920DEST_PATH_IMAGE040
is composed oftThe time interval direct-fired engine transmits power to the cold and hot energy buses,
Figure 100002_DEST_PATH_IMAGE079
a cost penalty function factor for the rejection of light,
Figure DEST_PATH_IMAGE080
the output is predicted for the photovoltaic system,
Figure 100002_DEST_PATH_IMAGE081
a cost penalty function factor for the wind curtailment,
Figure DEST_PATH_IMAGE082
and predicting the output for the wind energy.
In the user layer, aiming at minimizing the user expenditure, the objective function of the third optimization control model is specifically represented by the following formula:
Figure 945586DEST_PATH_IMAGE006
（6）
wherein,
Figure 470108DEST_PATH_IMAGE045
the energy consumption is paid for the user,
Figure 100002_DEST_PATH_IMAGE083
as a processing cost factor of the internal combustion engine,
Figure DEST_PATH_IMAGE084
for the cost factor of the output of the direct-fired engine,
Figure 774837DEST_PATH_IMAGE048
in order to maintain the coefficients for the thermal energy storage,
Figure 370904DEST_PATH_IMAGE049
in order to electrically cool the output cost coefficient,
Figure 100002_DEST_PATH_IMAGE085
is a cost coefficient for processing the ice storage battery.
For step S102, the multi-objective function is solved under constraint conditions to obtain optimized control parameters of the microgrid in the target area.
The optimization control method of the regional microgrid provided by the embodiment of the invention comprises the steps of firstly dividing a target regional microgrid into a main power distribution network layer, a micro-energy source network layer and a user layer, respectively establishing corresponding optimization control models for the main power distribution network layer, the micro-energy source network layer and the user layer, then comprehensively solving each optimization control model to obtain optimization control parameters of the target regional microgrid, and realizing comprehensive optimization of the comprehensive production cost and the energy consumption cost of the regional microgrid through multi-objective optimization planning; compared with the traditional optimization planning scheme of the power distribution network, the operation of the regional micro-grid is controlled based on the optimization control parameters, and the efficient and reasonable operation of the regional micro-grid is realized.
On the basis of the foregoing embodiment, in the optimization control method for a regional microgrid provided in the embodiment of the present invention, step S102: comprehensively solving each optimized control model to obtain optimized control parameters of the microgrid of the target area, which can be specifically as follows:
and respectively solving each optimized control model by using a genetic algorithm.
In a specific implementation, the maximum iteration number and the population number of each optimization control model are selected first.
In the first optimal control model, control parameters (including
Figure DEST_PATH_IMAGE086
、
Figure 100002_DEST_PATH_IMAGE087
、
Figure DEST_PATH_IMAGE088
、
Figure DEST_PATH_IMAGE089
、
Figure DEST_PATH_IMAGE090
、
Figure DEST_PATH_IMAGE091
、
Figure DEST_PATH_IMAGE092
) And obtaining different combination schemes as individuals, and generating an initial population according to a preset population quantity. Calculating individual fitness value of each individual in the initial population according to formula (6),and (4) performing tournament selection, single-point crossing and variation according to the individual fitness value to obtain a progeny population. The number of iterations is incremented by one. And (4) judging whether the iteration calculation ending condition is not met (the iteration times reach the maximum iteration times or the individual fitness value meets the target individual fitness value), and repeating the operation by taking the filial generation population as the parent population. Until the iterative computation end condition is met, outputting corresponding first optimization control parameters (including
Figure 785967DEST_PATH_IMAGE086
、
Figure 215811DEST_PATH_IMAGE087
、
Figure 50912DEST_PATH_IMAGE088
、
Figure 794877DEST_PATH_IMAGE089
、
Figure 516977DEST_PATH_IMAGE090
、
Figure 383301DEST_PATH_IMAGE091
、
Figure 49906DEST_PATH_IMAGE092
）。
Inputting the first optimized control parameter into the second optimized control model, recovering the iteration number, and initializing the output and control parameters (including output and control parameters) of each device of the micro energy network layer
Figure DEST_PATH_IMAGE093
、
Figure DEST_PATH_IMAGE094
) And obtaining different combination schemes as individuals, and generating an initial population according to a preset population quantity. Calculating individual fitness value of each individual in the initial population according to a formula (5), and performing tournament selection, single-point crossing and variation according to the individual fitness value to obtain offspring population. The number of iterations is incremented by one. And (4) judging whether the iteration calculation ending condition is not met (the iteration times reach the maximum iteration times or the individual fitness value meets the target individual fitness value), and repeating the operation by taking the filial generation population as the parent population. Until the iterative computation end condition is met, outputting corresponding second optimization control parameters (including
Figure DEST_PATH_IMAGE095
、
Figure 797895DEST_PATH_IMAGE094
）。
Inputting the second optimized control parameter into the third optimized control model, recovering the iteration number, and initializing the control parameter of the main power distribution network layer (
Figure DEST_PATH_IMAGE096
) An initial population is generated according to a predetermined population number. And (2) calculating the individual fitness value of each individual in the initial population according to a formula (1), and performing tournament selection, single-point crossing and variation according to the individual fitness value to obtain a progeny population. The number of iterations is incremented by one. And (4) judging whether the iteration calculation ending condition is not met (the iteration times reach the maximum iteration times or the individual fitness value meets the target individual fitness value), and repeating the operation by taking the filial generation population as the parent population. Until the iterative computation end condition is satisfied, outputting the corresponding third control parameter (c)
Figure DEST_PATH_IMAGE097
）。
Further, in order to accelerate the convergence speed of the algorithm, prevent the algorithm from getting early and reduce the algorithm from getting into local optimum, when the genetic algorithm is used for respectively solving each optimized control model, the simulated annealing algorithm is adopted to screen individuals in each iterative operation of each optimized control model.
Specifically, the individuals are screened by using a simulated annealing algorithm, and the probability of the selected individuals can be calculated by the following formula:
Figure DEST_PATH_IMAGE098
； （7）
wherein,
Figure 827031DEST_PATH_IMAGE052
the probability of being selected for an individual is,
Figure 598678DEST_PATH_IMAGE053
for the fitness value corresponding to the current decision variable,
Figure 893524DEST_PATH_IMAGE054
to generate a fitness value for the new decision variable,
Figure 244871DEST_PATH_IMAGE055
is the boltzmann constant, and is,
Figure 862934DEST_PATH_IMAGE056
is the annealing temperature.
To simplify the calculation, the method can be used
Figure 133378DEST_PATH_IMAGE053
The value of (2) is set as a constant 0, and according to the characteristic that the value of the optimization objective function of each level in the three-layer structure of the microgrid in the target area is certainly greater than 0, the formula (7) can be simplified as follows:
Figure DEST_PATH_IMAGE099
； （8）
at the user layer, taking the user layer model
Figure DEST_PATH_IMAGE100
The reciprocal of (a) is an individual fitness value; taking a model of the micro energy network layer
Figure DEST_PATH_IMAGE101
Is an individual fitness value; taking main power distribution from main power distribution network layerNet layer model
Figure DEST_PATH_IMAGE102
Is an individual fitness value. When annealing temperature
Figure DEST_PATH_IMAGE103
At the same time, the greater the fitness value at different target levels, the greater the probability that the individual is selected
Figure DEST_PATH_IMAGE104
The smaller is; annealing temperature at the very beginning of iteration
Figure 915520DEST_PATH_IMAGE103
The value of (A) is very high, the simulated annealing algorithm can be regarded as performing wide-area search, and the situation that the local optimization is trapped can be avoided; at the later iteration stage of the algorithm, the annealing temperature
Figure 680345DEST_PATH_IMAGE103
The value of (a) is very low, and the simulated annealing algorithm at this time can be considered to be performing local search, so that a refined local optimal solution can be obtained. Wherein the annealing temperature
Figure 152915DEST_PATH_IMAGE103
And controlling the solving process to be carried out towards the optimal value. Through the analysis, the parallel search capability of the genetic algorithm can provide a relatively large search range for the simulated annealing algorithm, the simulated annealing algorithm can greatly improve the local search capability of the genetic algorithm, and the simulated annealing algorithm and the genetic algorithm are combined and improved to obtain an improved simulated annealing-genetic (SA-GA) optimization algorithm with high efficiency and strong robustness.
On the basis of the detailed description of the various embodiments corresponding to the optimization control method of the regional microgrid, the invention also discloses an optimization control device, equipment and a readable storage medium of the regional microgrid corresponding to the method.
Fig. 4 is a schematic structural diagram of an optimization control device for a local microgrid provided in an embodiment of the present invention.
As shown in fig. 4, an optimization control apparatus for a local microgrid provided by an embodiment of the present invention includes:
the modeling unit 401 is configured to divide a target area microgrid into a main power distribution grid layer, a micro-energy grid layer and a user layer, and respectively establish corresponding optimal control models for the main power distribution grid layer, the micro-energy grid layer and the user layer;
a solving unit 402, configured to comprehensively solve each optimized control model to obtain optimized control parameters of the target area microgrid;
and a control unit 403, configured to control operation of the target area microgrid based on the optimized control parameter.
Since the embodiment of the apparatus portion and the embodiment of the method portion correspond to each other, please refer to the description of the embodiment of the method portion for the embodiment of the apparatus portion, and details are not repeated here.
Fig. 5 is a schematic structural diagram of an optimization control device of a local microgrid provided in an embodiment of the present invention.
As shown in fig. 5, an optimization control apparatus for a regional microgrid provided in an embodiment of the present invention includes:
a memory 510 for storing instructions, the instructions including the steps of the method for optimizing and controlling a local microgrid according to any one of the above embodiments;
a processor 520 for executing the instructions.
Among other things, processor 520 may include one or more processing cores, such as a 3-core processor, an 8-core processor, and so on. The processor 520 may be implemented in at least one hardware form of a Digital Signal Processing (DSP), a Field-Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), or a Programmable Logic Array (PLA). Processor 520 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a central Processing unit (cpu); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 520 may be integrated with an image processor GPU (graphics Processing unit) that is responsible for rendering and drawing the content that the display screen needs to display. In some embodiments, processor 520 may also include an Artificial Intelligence (AI) (artificial intelligence) processor for processing computational operations related to machine learning.
Memory 510 may include one or more readable storage media, which may be non-transitory. Memory 510 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory 510 is at least used for storing a computer program 511, wherein after the computer program 511 is loaded and executed by the processor 520, the relevant steps in the optimization control method for the regional microgrid disclosed in any one of the foregoing embodiments can be implemented. In addition, the resources stored in the memory 510 may also include an operating system 512, data 513, and the like, and the storage manner may be a transient storage or a permanent storage. The operating system 512 may be Windows, among others. Data 513 may include, but is not limited to, data involved with the above-described methods.
In some embodiments, the optimization control device for the regional microgrid may further include a display screen 530, a power source 540, a communication interface 550, an input-output interface 560, a sensor 570, and a communication bus 580.
Those skilled in the art will appreciate that the configuration shown in fig. 5 does not constitute a limitation of the optimized control device of the regional microgrid, and may include more or fewer components than those shown.
The optimization control device for the regional microgrid provided by the embodiment of the application comprises a memory and a processor, and when the processor executes a program stored in the memory, the optimization control method for the regional microgrid can be realized, and the effects are the same as above.
It should be noted that the above-described embodiments of the apparatus and device are merely illustrative, for example, the division of modules is only one division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, indirect coupling or communication connection between devices or modules, and may be in an electrical, mechanical or other form. Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may be stored in a readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and performs all or part of the steps of the methods according to the embodiments of the present invention, or all or part of the technical solution.
To this end, an embodiment of the present invention further provides a readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the optimization control method, such as the regional microgrid.
The readable storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory ROM (Read-Only Memory), a random Access Memory ram (random Access Memory), a magnetic disk, or an optical disk.
The readable storage medium provided in this embodiment contains a computer program, which when executed by a processor, is capable of implementing the steps of the method for optimizing and controlling a local microgrid described above, and the effects are the same as above.
The method, the device, the equipment and the readable storage medium for optimizing and controlling the regional microgrid provided by the invention are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device, the apparatus and the readable storage medium disclosed by the embodiments correspond to the method disclosed by the embodiments, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

Claims (8)

1. An optimization control method for a regional microgrid is characterized by comprising the following steps:
dividing a target area microgrid into a main power distribution network layer, a micro-energy source network layer and a user layer, and respectively establishing corresponding optimized control models for the main power distribution network layer, the micro-energy source network layer and the user layer;
comprehensively solving each optimized control model to obtain optimized control parameters of the microgrid in the target area;
controlling the operation of the micro-grid of the target area based on the optimized control parameters;
the establishing of the corresponding optimization control models for the main power distribution network layer, the micro-energy network layer and the user layer respectively specifically includes:
establishing a first optimization control model aiming at maximizing the income of a power supply company for the main power distribution network layer;
establishing a second optimization control model aiming at minimizing energy consumption for the micro energy resource network layer;
establishing a third optimization control model aiming at minimizing user energy expenditure for the user layer;
correspondingly, the comprehensive solution of each optimal control model to obtain the optimal control parameters of the target area microgrid specifically comprises:
inputting the initial unit state parameters of the micro-grid in the target area into the third optimization control model, and then solving the third optimization control model to obtain third optimization control parameters;
after the third optimization control parameter is input into the second optimization control model, solving the second optimization control model to obtain a second optimization control parameter;
after the second optimization control parameter is input into the first optimization control model, solving the first optimization control model to obtain a first optimization control parameter;
the objective function of the first optimal control model is specifically represented by the following formula:
Figure DEST_PATH_IMAGE001
；
the constraint condition of the first optimization control model is specifically represented by the following formula:
Figure DEST_PATH_IMAGE003
；
Figure 149724DEST_PATH_IMAGE004
；
Figure DEST_PATH_IMAGE005
；
the objective function of the second optimal control model is specifically represented by the following formula:
Figure 591200DEST_PATH_IMAGE006
；
the objective function of the third optimization control model is specifically represented by the following formula:
Figure DEST_PATH_IMAGE007
；
wherein,
Figure 689606DEST_PATH_IMAGE008
in order to be a profit for the power supply company,
Figure DEST_PATH_IMAGE009
is composed oftThe unit electricity rate of the time period,
Figure 131739DEST_PATH_IMAGE010
is composed oftThe generated power of the time period is,
Figure 987699DEST_PATH_IMAGE012
is composed oftThe power generation cost of the generator set of the target area microgrid,
Figure DEST_PATH_IMAGE013
is composed oft(ii) emissions cost of the generator set over time;
Figure 950976DEST_PATH_IMAGE014
for the amount of electricity sold by the power supply company,
Figure 310413DEST_PATH_IMAGE016
is composed oftThe photovoltaic power generation is carried out in a time period,
Figure DEST_PATH_IMAGE017
is composed oftThe fan generates electricity and outputs power in a time period,
Figure 654938DEST_PATH_IMAGE018
is composed oftThe internal combustion engine generates power in a time interval,
Figure DEST_PATH_IMAGE019
is composed oftThe discharge state value of the storage battery in the time period,
Figure 122829DEST_PATH_IMAGE020
is composed oftThe period of time is the discharge power of the battery,
Figure DEST_PATH_IMAGE021
is composed oftThe actual electrical load of the user over the period of time,
Figure 843791DEST_PATH_IMAGE022
is composed oftThe time interval is the power consumption of the refrigerating machine,
Figure 120052DEST_PATH_IMAGE024
is composed oftThe ice-making state value of the time period,
Figure 291270DEST_PATH_IMAGE026
is composed oftThe electric power consumed by the ice making in the time interval ice storage tank,
Figure DEST_PATH_IMAGE027
is composed oftThe value of the ice-melt state for a time period,
Figure 246456DEST_PATH_IMAGE028
is composed oftThe power consumption of the ice melting in a time interval,
Figure 161323DEST_PATH_IMAGE030
is composed oftThe time period is a value of a state of charge of the battery,
Figure DEST_PATH_IMAGE031
is composed oftTime-interval charging power of the battery;
Figure 636298DEST_PATH_IMAGE032
is composed oftThe heat-generating power of the time-interval heat exchanger,
Figure DEST_PATH_IMAGE033
is composed oftThe heat-generating power of the direct-fired engine in a time period,
Figure 571893DEST_PATH_IMAGE034
is composed oftThe heat release state value of the time-zone heat storage device,
Figure 420900DEST_PATH_IMAGE036
is composed oftThe period of time is the heat-releasing power of the thermal storage device,
Figure DEST_PATH_IMAGE037
is composed oftThe actual heat load of the user over the period of time,
Figure 480735DEST_PATH_IMAGE038
is composed oftThe heat storage state value of the heat storage device is set for a period of time,
Figure DEST_PATH_IMAGE039
is composed oftThe heat storage power of the heat storage device is set in time interval;
Figure 934850DEST_PATH_IMAGE040
is composed oftThe cooling power of the direct-fired engine is divided into a plurality of time periods,
Figure DEST_PATH_IMAGE041
is composed oftDuring the period of time the refrigerating power of said refrigerator,
Figure 41347DEST_PATH_IMAGE042
is composed oftThe ice-melting cooling power of the time period,
Figure 377650DEST_PATH_IMAGE044
is composed oftActual cooling load of the user over time;
Figure 775265DEST_PATH_IMAGE046
for the energy consumption of the micro-energy net layer,
Figure DEST_PATH_IMAGE047
is a cost factor for the output of the internal combustion engine,
Figure 411782DEST_PATH_IMAGE048
is the cost coefficient of the direct combustion engine output,
Figure DEST_PATH_IMAGE049
is composed oftThe power transmitted to the cold and hot energy buses by the direct-fired machine in a time period,
Figure 361284DEST_PATH_IMAGE050
a cost penalty function factor for the rejection of light,
Figure DEST_PATH_IMAGE051
the output is predicted for the photovoltaic system,
Figure 263512DEST_PATH_IMAGE052
a cost penalty function factor for the wind curtailment,
Figure DEST_PATH_IMAGE053
predicting the output for the wind energy;
Figure 917347DEST_PATH_IMAGE054
the energy consumption is paid for the user,
Figure DEST_PATH_IMAGE055
as a processing cost factor of the internal combustion engine,
Figure 346054DEST_PATH_IMAGE056
is the output cost coefficient of the direct-fired engine,
Figure DEST_PATH_IMAGE057
in order to maintain the coefficients for the thermal energy storage,
Figure 341823DEST_PATH_IMAGE058
in order to electrically cool the output cost coefficient,
Figure DEST_PATH_IMAGE059
is a cost coefficient for processing the ice storage battery.
2. The optimization control method according to claim 1, wherein the comprehensive solution of each optimization control model obtains the optimization control parameters of the target area microgrid, and specifically comprises:
and respectively solving each optimized control model by using a genetic algorithm.
3. The optimization control method according to claim 2, wherein the respective optimization control models are solved by using a genetic algorithm, specifically:
and respectively solving each optimized control model by using a genetic algorithm, and screening individuals by using a simulated annealing algorithm in each iterative operation of each optimized control model.
4. The optimization control method according to claim 3, wherein the individuals are screened by the simulated annealing algorithm, and specifically, the probability of the individual being selected is calculated by the following formula:
Figure 183877DEST_PATH_IMAGE060
；
wherein,
Figure DEST_PATH_IMAGE061
the probability of being selected for an individual is,
Figure DEST_PATH_IMAGE063
for the fitness value corresponding to the current decision variable,
Figure 406784DEST_PATH_IMAGE064
to generate a fitness value for the new decision variable,
Figure DEST_PATH_IMAGE065
is the boltzmann constant, and is,
Figure 689998DEST_PATH_IMAGE066
is the annealing temperature.
5. The optimization control method according to claim 3, wherein the individuals are screened by the simulated annealing algorithm, and specifically, the probability of the individual being selected is calculated by the following formula:
Figure DEST_PATH_IMAGE067
；
wherein,
Figure 371515DEST_PATH_IMAGE061
the probability of being selected for an individual is,
Figure 107390DEST_PATH_IMAGE063
to generate a fitness value for the new decision variable,
Figure 384919DEST_PATH_IMAGE065
is the boltzmann constant, and is,
Figure 53797DEST_PATH_IMAGE066
is the annealing temperature.
6. An optimization control device for a regional microgrid, comprising:
the modeling unit is used for dividing a target area microgrid into a main power distribution network layer, a micro-energy source network layer and a user layer and respectively establishing corresponding optimized control models for the main power distribution network layer, the micro-energy source network layer and the user layer;
the solving unit is used for comprehensively solving each optimized control model to obtain optimized control parameters of the micro-grid in the target area;
the control unit is used for controlling the operation of the micro-grid of the target area based on the optimized control parameters;
the establishing of the corresponding optimization control models for the main power distribution network layer, the micro-energy network layer and the user layer respectively specifically includes:
establishing a first optimization control model aiming at maximizing the income of a power supply company for the main power distribution network layer;
establishing a second optimization control model aiming at minimizing energy consumption for the micro energy resource network layer;
establishing a third optimization control model aiming at minimizing user energy expenditure for the user layer;
correspondingly, the comprehensive solution of each optimal control model to obtain the optimal control parameters of the target area microgrid specifically comprises:
inputting the initial unit state parameters of the micro-grid in the target area into the third optimization control model, and then solving the third optimization control model to obtain third optimization control parameters;
after the third optimization control parameter is input into the second optimization control model, solving the second optimization control model to obtain a second optimization control parameter;
after the second optimization control parameter is input into the first optimization control model, solving the first optimization control model to obtain a first optimization control parameter;
the objective function of the first optimal control model is specifically represented by the following formula:
Figure 781582DEST_PATH_IMAGE001
；
the constraint condition of the first optimization control model is specifically represented by the following formula:
Figure DEST_PATH_IMAGE069
；
Figure 332649DEST_PATH_IMAGE004
；
Figure 335240DEST_PATH_IMAGE005
；
the objective function of the second optimization control model is specifically represented by the following formula:
Figure 937254DEST_PATH_IMAGE006
；
the objective function of the third optimization control model is specifically represented by the following formula:
Figure 835940DEST_PATH_IMAGE070
；
wherein,
Figure 77565DEST_PATH_IMAGE008
in order to be a benefit of the power supply company,
Figure 946164DEST_PATH_IMAGE009
is composed oftThe unit electricity rate of the time period,
Figure 58477DEST_PATH_IMAGE010
is composed oftThe generated power of the time period is,
Figure 659222DEST_PATH_IMAGE072
is composed oftThe power generation cost of the generator set of the target area microgrid,
Figure 732352DEST_PATH_IMAGE013
is composed oft(ii) emissions cost of the generator set over time;
Figure DEST_PATH_IMAGE073
for the amount of electricity sold by the power supply company,
Figure DEST_PATH_IMAGE075
is composed oftThe photovoltaic power generation is carried out in a time interval,
Figure 607904DEST_PATH_IMAGE017
is composed oftThe fan generates electricity and outputs power in a time period,
Figure 574723DEST_PATH_IMAGE018
is composed oftThe internal combustion engine generates power in a time interval,
Figure 346370DEST_PATH_IMAGE019
is composed oftThe discharge state value of the storage battery in the time period,
Figure 903865DEST_PATH_IMAGE020
is composed oftTime interval of discharge of the storage batteryThe electric power is supplied to the electric motor,
Figure 989633DEST_PATH_IMAGE021
is composed oftThe actual electrical load of the user over the period of time,
Figure 935592DEST_PATH_IMAGE076
is composed oftThe time interval is the power consumption of the refrigerating machine,
Figure 143720DEST_PATH_IMAGE024
is composed oftThe ice-making state value of the time period,
Figure 784917DEST_PATH_IMAGE026
is composed oftThe electric power consumed by the ice making of the time interval ice storage tank,
Figure DEST_PATH_IMAGE077
is composed oftThe value of the ice-melt state for a time period,
Figure 18583DEST_PATH_IMAGE028
is composed oftThe power consumption of the ice melting in the time interval,
Figure 84628DEST_PATH_IMAGE030
is composed oftThe time period is a value of a state of charge of the battery,
Figure 198078DEST_PATH_IMAGE078
is composed oftTime-interval charging power of the battery;
Figure 326571DEST_PATH_IMAGE032
is composed oftThe heat-generating power of the time-interval heat exchanger,
Figure DEST_PATH_IMAGE079
is composed oftThe heat-generating power of the direct-fired engine in a time period,
Figure 160666DEST_PATH_IMAGE080
is composed oftThe heat release state value of the time-period heat storage device,
Figure 956583DEST_PATH_IMAGE036
is composed oftThe heat-releasing power of the heat storage device is over a period of time,
Figure DEST_PATH_IMAGE081
is composed oftThe actual heat load of the user over the period of time,
Figure 37672DEST_PATH_IMAGE038
is composed oftThe value of the heat storage state of the heat storage device over a period of time,
Figure 715778DEST_PATH_IMAGE039
is composed oftThe heat storage power of the heat storage device is set in time interval;
Figure 556826DEST_PATH_IMAGE040
is composed oftThe cooling power of the direct-fired engine is divided into a plurality of time periods,
Figure 207250DEST_PATH_IMAGE041
is composed oftDuring the period of time the refrigerating power of said refrigerator,
Figure 928081DEST_PATH_IMAGE042
is composed oftThe ice-melting cooling power of the time period,
Figure 155800DEST_PATH_IMAGE044
is composed oftActual cooling load of the time period user;
Figure DEST_PATH_IMAGE083
for the energy consumption of the micro-energy net layer,
Figure 456332DEST_PATH_IMAGE047
is a cost factor for the output of the internal combustion engine,
Figure 573979DEST_PATH_IMAGE048
is the cost coefficient of the direct combustion engine output,
Figure 668974DEST_PATH_IMAGE049
is composed oftThe power transmitted to the cold and hot energy buses by the direct-fired machine in a time period,
Figure 321672DEST_PATH_IMAGE050
a cost penalty function factor for the rejection,
Figure 19370DEST_PATH_IMAGE051
the output is predicted for the photovoltaic system,
Figure 378807DEST_PATH_IMAGE052
a cost penalty function factor for the wind curtailment,
Figure 441441DEST_PATH_IMAGE084
predicting the output for the wind energy;
Figure 394485DEST_PATH_IMAGE054
the energy consumption is paid for the user,
Figure 771239DEST_PATH_IMAGE055
as a processing cost factor of the internal combustion engine,
Figure DEST_PATH_IMAGE085
as a cost out factor for said direct combustion engine,
Figure 640975DEST_PATH_IMAGE086
in order to maintain the coefficient of heat storage energy,
Figure 812194DEST_PATH_IMAGE058
in order to electrically cool the output cost coefficient,
Figure DEST_PATH_IMAGE087
is a cost coefficient for processing the ice storage battery.
7. An optimal control device for a regional microgrid, comprising:
a memory for storing instructions comprising the steps of the method for optimizing control of a regional microgrid according to any one of claims 1 to 5;
a processor to execute the instructions.
8. A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for the optimized control of a regional microgrid according to any one of claims 1 to 5.
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