CN111342462A - Microgrid optimization scheduling system, method, storage medium and computer program - Google Patents

Microgrid optimization scheduling system, method, storage medium and computer program Download PDF

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CN111342462A
CN111342462A CN202010247352.2A CN202010247352A CN111342462A CN 111342462 A CN111342462 A CN 111342462A CN 202010247352 A CN202010247352 A CN 202010247352A CN 111342462 A CN111342462 A CN 111342462A
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microgrid
fault
module
grid
power
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袁地
张颖新
田俊龙
耿会娟
闫志诚
胡其成
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Anyang Normal 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • 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
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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Abstract

The invention belongs to the technical field of microgrid optimization and discloses a microgrid optimization scheduling system, a microgrid optimization scheduling method, a storage medium and a computer program, wherein the microgrid optimization scheduling system comprises the following components: the power grid fault diagnosis system comprises a current detection module, a voltage detection module, a power grid parameter configuration module, a main control module, a power grid optimization module, a power grid scheduling module, a power grid fault diagnosis module, a cloud processing module, a data storage module and a display module. According to the method, the capacity of the microgrid power supply is optimally configured by using an improved hybrid particle swarm algorithm, an island microgrid capacity optimal configuration model with the target of actual comprehensive investment cost, comprehensive reliability, residual energy rate and renewable energy rate is established, the operation of a microgrid system can be actually simulated according to different operation criteria of a micro gas turbine and a storage battery, a final optimal capacity configuration scheme is given, the configuration precision is high, the calculation times of inertia weight in the iteration process are effectively reduced, and the optimization efficiency is improved.

Description

Microgrid optimization scheduling system, method, storage medium and computer program
Technical Field
The invention belongs to the technical field of microgrid optimization, and particularly relates to a microgrid optimization scheduling system, a microgrid optimization scheduling method, a storage medium and a computer program.
Background
The Micro-Grid (Micro-Grid) is also translated into a Micro-Grid, which refers to a small power generation and distribution system composed of a distributed power supply, an energy storage device, an energy conversion device, a load, a monitoring and protecting device and the like. The micro-grid aims to realize flexible and efficient application of distributed power supplies and solve the problem of grid connection of the distributed power supplies with large quantity and various forms. The development and extension of the micro-grid can fully promote the large-scale access of distributed power sources and renewable energy sources, realize the high-reliability supply of various energy source types of loads, and is an effective mode for realizing an active power distribution network, so that the traditional power grid is transited to a smart power grid. However, the existing microgrid optimal scheduling system and method have poor optimization effect on power grid configuration and low efficiency; meanwhile, the power grid is unreasonably scheduled.
In summary, the problems of the prior art are as follows: the existing micro-grid optimal scheduling system and method have poor effect of optimizing the power grid configuration and low efficiency; meanwhile, the power grid is unreasonably scheduled.
Disclosure of Invention
The invention provides a microgrid optimization scheduling system, a microgrid optimization scheduling method, a storage medium and a computer program.
The invention is realized in such a way that a microgrid optimization scheduling method comprises the following steps:
step one, optimizing the configuration of the microgrid according to configured microgrid parameters through a power grid optimization program: (I) acquiring a power sequence and a load sequence of a wind driven generator and a solar photovoltaic cell through power detection equipment;
(II) establishing an island microgrid capacity optimization configuration model by taking the comprehensive investment cost, the comprehensive reliability, the residual energy rate and the renewable energy source rate as planning targets;
and (III) solving the capacity optimization configuration model of the island microgrid by adopting an improved hybrid particle swarm algorithm based on the step (I) to obtain an optimal configuration scheme.
Step two, scheduling the optimized micro-grid power supply through a power grid scheduling program: (1) constructing a micro-grid optimization mathematical model, giving a calculation mode of grid output power, starting cost and operation and maintenance cost through the mathematical model, and giving grid power generation cost and environmental management cost; the distributed power supply comprises an uncontrollable micro power supply and a controllable micro power supply;
(2) the method comprises the steps that a microgrid economic dispatching model is built through a mathematical model, a dispatching optimization strategy which preferentially utilizes all generated energy of an uncontrollable microgrid and a diesel generator as a standby power supply is adopted by taking the minimum power generation cost and the minimum environmental management cost as objective functions, and electric energy interaction is carried out with a large power grid only when the microgrid cannot meet the load requirement or the microgrid has excessive power;
(3) adjusting the state of the particles by self experience and group experience through a particle swarm optimization algorithm to optimize system parameters, and constructing an improved particle swarm optimization algorithm; the initial positions of the particles in the particle swarm are randomly generated in the search area, and the speed of each particle is randomly given; applying a particle swarm algorithm to a program, wherein each particle moves at a solution space position once, and then optimizing is repeatedly performed after one iterative process is completed until one of the following conditions is met: the particles are relatively stationary in solution space or reach a maximum number of iterations.
Step three, diagnosing the micro-grid fault through a fault diagnosis circuit: 1) constructing a power grid full model;
2) acquiring fault information of a feeder line, and positioning the feeder line where the fault is located according to the fault information and the power grid full model;
3) acquiring fault indication information of a fault indicator, and positioning a first fault section where a fault is located according to the fault indication information and the power grid full model;
4) acquiring a distribution transformer power failure event of a distribution transformer, and positioning first tripping equipment where a fault is located according to the distribution transformer power failure event and the power grid full model;
5) acquiring measurement sudden drop information of an outgoing line switch, and positioning a second fault interval and second tripping equipment where a fault is located according to the measurement sudden drop information and the power grid full model;
6) generating a diagnosis report according to the feeder line, the first fault interval, the first trip device, the second fault interval and the second trip device obtained by positioning
7) And outputting fault comprehensive information and a diagnosis report, wherein the fault comprehensive information comprises the first fault interval, the first trip equipment, the second fault interval and the second trip equipment.
Further, before the step one, the following steps are required: step I, detecting the power supply current data of the microgrid through an ammeter;
step II, detecting the power supply voltage data of the microgrid through a voltmeter;
step III, carrying out parameter configuration on the microgrid through a configuration program according to the detected current and voltage supplied by the microgrid;
after the third step, the following steps are required:
step 1, processing microgrid data by using cloud computing through a cloud server;
step 2, storing the current, the voltage, the configuration data and the real-time data of the fault diagnosis result of the microgrid through a memory;
and 3, displaying the detected current, voltage and configuration data of the microgrid and real-time data of the fault diagnosis result through a display.
Further, in the first step, the island microgrid capacity optimization configuration model in the step (III) is specifically described as:
minF(x)＝w1C+w3ηLPSP+w2fEER+w4fre
s.t.SOCmin≤SOC(t)≤SOCmax
Pi，min≤Pi(t)≤Pi，min
Ebs，rest≥ηEgen，rest
ηLPSP≥ηLPSP，min
fre≥fre，min
wherein C is the total annual average cost of the micro-grid investment ηLPSPAnd ηLPSP，minRespectively, the comprehensive reliability and the lower limit of the comprehensive reliability; f. ofEERIs the remaining energy rate; f. ofreAnd fre，minRespectively, the renewable energy rate and the renewable energy rate lower limit; SOC (t), SOCmin、SOCmaxThe state of the charge quantity of the storage battery and the minimum value and the maximum value of the charge quantity are respectively; pi (t), Pi,min、Pi,maxRespectively the output power of the micro power supply and the lower limit and the upper limit of the output power; ebs,restIs the remaining chargeable quantity of the storage battery; egen,restThe surplus of the electric energy required by the load is subtracted from the generated electric energy, η is the charging efficiency of the storage battery, w1、w2、w3、w4The weight coefficients of the targets are respectively; x is a decision variable.
Further, in the second step, the economic dispatching model of the microgrid proposed in the step (1) takes the power generation cost and the environmental governance cost as objective functions, takes power constraint as constraint conditions and proposes a dispatching strategy of the preferential output of the uncontrollable micro power supply.
Further, in the third step, the fault indication information includes card turning action information, a fault current value and a load current value;
the diagnosis processing on the corresponding feeder line specifically includes:
acquiring the current value of the distribution transformer of the corresponding feeder line;
and positioning the first tripping equipment where the fault is located according to the current value of the distribution transformer and the full power grid model.
Further, in step three, the method for acquiring the power distribution outage event in step 4) specifically includes:
detecting the distribution transformer blackout event of the distribution transformer;
when the distribution transformer power failure event is triggered, acquiring the distribution transformer power failure event of the distribution transformer;
and triggering diagnosis processing of the corresponding feeder line according to the power distribution outage event.
Further, in step three, the method for acquiring the measurement dip information in step 5) is as follows:
obtaining the measurement sudden drop proportion of the outgoing line switches;
detecting whether the measurement sudden drop proportion is larger than a preset threshold value or not, and positioning a second fault interval and second tripping equipment according to the outgoing line switch and the power grid full model when the measurement sudden drop proportion of the outgoing line switch is larger than the preset threshold value;
and when the measurement sudden-drop ratio of a certain outgoing switch is detected to be larger than a preset threshold value, obtaining the measurement information of the two-remote and three-remote switches, the distribution transformer and the fault indicator on the feeder line corresponding to the outgoing switch, and positioning a second fault interval and second tripping equipment according to the measurement information of the two-remote and three-remote switches, the distribution transformer and the fault indicator and the full model of the power grid.
Another object of the present invention is to provide a microgrid optimization scheduling system using the microgrid optimization scheduling method, wherein the microgrid optimization scheduling system includes:
the power grid fault diagnosis system comprises a current detection module, a voltage detection module, a power grid parameter configuration module, a main control module, a power grid optimization module, a power grid scheduling module, a power grid fault diagnosis module, a cloud processing module, a data storage module and a display module.
The current detection module is connected with the main control module and used for detecting the power supply current data of the micro-grid through the ammeter;
the voltage detection module is connected with the main control module and used for detecting the power supply voltage data of the micro-grid through a voltmeter;
the power grid parameter configuration module is connected with the main control module and used for carrying out parameter configuration on the micro-grid according to the detected current and voltage supplied by the micro-grid through a configuration program;
the main control module is connected with the current detection module, the voltage detection module, the power grid parameter configuration module, the power grid optimization module, the power grid scheduling module, the power grid fault diagnosis module, the cloud processing module, the data storage module and the display module and is used for controlling each module to normally work through the main control computer;
the power grid optimization module is connected with the main control module and used for optimizing the configuration of the micro-grid according to the configured micro-grid parameters through a power grid optimization program;
the power grid dispatching module is connected with the main control module and used for dispatching the optimized micro-grid power supply through a power grid dispatching program;
the power grid fault diagnosis module is connected with the main control module and used for diagnosing the micro-grid fault through the fault diagnosis circuit;
the cloud processing module is connected with the main control module and used for processing the microgrid data by utilizing cloud computing through the cloud server;
the data storage module is connected with the main control module and used for storing the current, the voltage, the configuration data and the real-time data of the fault diagnosis result of the microgrid through the memory;
and the display module is connected with the main control module and used for displaying the detected current, voltage, configuration data and real-time data of the fault diagnosis result of the microgrid through the display.
Another object of the present invention is to provide a computer program product stored on a computer readable medium, which includes a computer readable program for providing a user input interface to implement the microgrid optimized scheduling method when the computer program product is executed on an electronic device.
Another object of the present invention is to provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to execute the microgrid optimized scheduling method.
The invention has the advantages and positive effects that: according to the method, the capacity of the micro-grid power supply is optimized and configured by the power grid optimization module through an improved hybrid particle swarm algorithm, an island micro-grid capacity optimization configuration model with the target of actual comprehensive investment cost, comprehensive reliability, residual energy rate and renewable energy rate is established, the micro-grid system can be actually simulated to operate according to different operation criteria of the micro gas turbine and the storage battery, a final optimal capacity configuration scheme is given, and the configuration precision is high; the adopted improved hybrid particle swarm optimization is combined with chaotic optimization and population variation to realize global traversal, so that search has pseudo-randomness, and a terrain knowledge evaluation mechanism is introduced to guide an individual to carry out rapid optimization with grades and directions so as to accelerate convergence performance; gaussian disturbance is adapted through a self-adaptive cosine chaotic variation threshold method, and finally, elite individuals are selected by combining chaotic mapping and natural selection operation in a genetic algorithm, so that the population superiority is maintained, and the algorithm robustness is improved; according to the invention, the inertia weight coefficient and the learning factor adjustment strategy are added in the parameter updating process, so that the calculation times of the inertia weight in the iteration process are effectively reduced, and the optimization efficiency is improved; meanwhile, a mathematical model of each distributed power supply is established through a power grid scheduling module, then an economic scheduling model of the micro-grid is established by considering the operation economic index and relevant constraint conditions of the micro-grid according to scheduling criteria, and two objective functions of power generation cost and environmental management cost and each constraint condition are provided; and then a program for reasonably arranging the output of each distributed power supply in the microgrid is compiled based on a particle swarm optimization algorithm, so that the aim of economically scheduling the microgrid is fulfilled.
Drawings
Fig. 1 is a flowchart of a microgrid optimization scheduling method provided in an embodiment of the present invention.
Fig. 2 is a block diagram of a microgrid optimization scheduling system according to an embodiment of the present invention;
in the figure: 1. a current detection module; 2. a voltage detection module; 3. a power grid parameter configuration module; 4. a main control module; 5. a power grid optimization module; 6. a power grid dispatching module; 7. a power grid fault diagnosis module; 8. a cloud processing module; 9. a data storage module; 10. and a display module.
Fig. 3 is a flowchart of a method for optimizing a microgrid configuration according to configured microgrid parameters by a power grid optimization program according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for scheduling optimized microgrid power supply through a power grid scheduling program according to an embodiment of the present invention.
Fig. 5 is a flowchart of a method for diagnosing a fault of a microgrid through a fault diagnosis circuit according to an embodiment of the present invention.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the microgrid optimization scheduling method provided by the embodiment of the present invention includes the following steps:
s101, detecting the power supply current data of the microgrid through an ammeter; and detecting the power supply voltage data of the microgrid through a voltmeter.
S102, carrying out parameter configuration on the microgrid through a configuration program according to the detected current and voltage supplied by the microgrid; and the main control machine is used for controlling the micro-grid optimization scheduling system to normally work.
S103, optimizing the configuration of the micro-grid according to the configured parameters of the micro-grid through a power grid optimization program; and scheduling the optimized micro-grid power supply through a power grid scheduling program.
S104, diagnosing the fault of the micro-grid through a fault diagnosis circuit; and processing the microgrid data by using cloud computing through a cloud server.
And S105, storing the current, the voltage, the configuration data and the real-time data of the fault diagnosis result of the microgrid through a memory.
And S106, displaying the detected current, voltage, configuration data and real-time data of the fault diagnosis result of the microgrid through a display.
As shown in fig. 2, the microgrid optimization scheduling system provided by the embodiment of the present invention includes: the system comprises a current detection module 1, a voltage detection module 2, a power grid parameter configuration module 3, a main control module 4, a power grid optimization module 5, a power grid scheduling module 6, a power grid fault diagnosis module 7, a cloud processing module 8, a data storage module 9 and a display module 10.
The current detection module 1 is connected with the main control module 4 and used for detecting the power supply current data of the micro-grid through an ammeter;
the voltage detection module 2 is connected with the main control module 4 and used for detecting the micro-grid power supply voltage data through a voltmeter;
the power grid parameter configuration module 3 is connected with the main control module 4 and is used for carrying out parameter configuration on the micro-grid according to the detected current and voltage supplied by the micro-grid through a configuration program;
the main control module 4 is connected with the current detection module 1, the voltage detection module 2, the power grid parameter configuration module 3, the power grid optimization module 5, the power grid scheduling module 6, the power grid fault diagnosis module 7, the cloud processing module 8, the data storage module 9 and the display module 10, and is used for controlling each module to normally work through the main control computer;
the power grid optimization module 5 is connected with the main control module 4 and used for optimizing the configuration of the micro-grid according to the configured micro-grid parameters through a power grid optimization program;
the power grid dispatching module 6 is connected with the main control module 4 and used for dispatching the optimized micro-grid power supply through a power grid dispatching program;
the power grid fault diagnosis module 7 is connected with the main control module 4 and is used for diagnosing the micro-grid fault through the fault diagnosis circuit;
the cloud processing module 8 is connected with the main control module 4 and used for processing the microgrid data by utilizing cloud computing through a cloud server;
the data storage module 9 is connected with the main control module 4 and used for storing the current, the voltage, the configuration data and the real-time data of the fault diagnosis result of the microgrid through a memory;
and the display module 10 is connected with the main control module 4 and is used for displaying the detected current, voltage, configuration data and real-time data of the fault diagnosis result of the microgrid through a display.
The invention is further described with reference to specific examples.
Example 1
As shown in fig. 1 and fig. 3, the method for optimizing a microgrid configuration according to configured microgrid parameters by using a power grid optimization program according to an embodiment of the present invention includes:
s201, acquiring a power sequence and a load sequence of the wind driven generator and the solar photovoltaic cell through power detection equipment.
S202, establishing an island micro-grid capacity optimization configuration model by taking the comprehensive investment cost, the comprehensive reliability, the residual energy rate and the renewable energy source rate as planning targets.
And S203, based on the S203, solving the capacity optimization configuration model of the island microgrid by adopting an improved hybrid particle swarm algorithm to obtain an optimal configuration scheme.
The island microgrid capacity optimization configuration model of step S203 provided in the embodiment of the present invention is specifically described as follows:
minF (x) di w1C+w3ηLPSP+w2fEER+W4fre
S.t.SOCmin≤SOC(t)≤SOCmax
Pi，min≤Pi(t)≤Pi，max
Ebs，res1≥ηEgen，rest
ηLPSP≥ηLPSP，min
fre≥fre，min
Wherein C is the total annual average cost of the micro-grid investment ηLPSPAnd ηLPSP，minRespectively, the comprehensive reliability and the lower limit of the comprehensive reliability; f. ofEERIs the remaining energy rate; f. ofreAnd fre，minRespectively, the renewable energy rate and the renewable energy rate lower limit; SOC (t), SOCmin、SOCmaxState of charge and amount of charge of the battery, respectivelyMinimum and maximum values of; pi (t), Pi，min、Pi，maxRespectively the output power of the micro power supply and the lower limit and the upper limit of the output power; ebs，restIs the remaining chargeable quantity of the storage battery; egen，restThe surplus of the electric energy required by the load is subtracted from the generated electric energy, η is the charging efficiency of the storage battery, w1、w2、w3、w4The weight coefficients of the targets are respectively; x is a decision variable.
Example 2
As shown in fig. 1 and fig. 4, the method for optimizing and scheduling a microgrid power supply according to an embodiment of the present invention includes:
s301, constructing a micro-grid optimization mathematical model, giving a calculation mode of grid output power, starting cost and operation maintenance cost through the mathematical model, and giving grid power generation cost and environmental management cost; the distributed power supply comprises an uncontrollable micro power supply and a controllable micro power supply.
S302, constructing a microgrid economic dispatching model through a mathematical model, taking the minimum power generation cost and the minimum environmental management cost as objective functions, adopting a dispatching optimization strategy which preferentially utilizes all the power generation amount of the uncontrollable microgrid and takes a diesel generator as a standby power supply, and carrying out electric energy interaction with the large power grid only when the microgrid cannot meet the load requirement or the power of the microgrid is excessive.
S303, adjusting the state of the particles by self experience and group experience through a particle swarm optimization algorithm to optimize system parameters, and constructing an improved particle swarm optimization algorithm; the initial positions of the particles in the particle swarm are randomly generated in the search area, and the speed of each particle is randomly given; applying a particle swarm algorithm to a program, wherein each particle moves at a solution space position once, and then optimizing is repeatedly performed after one iterative process is completed until one of the following conditions is met: the particles are relatively stationary in solution space or reach a maximum number of iterations.
In the microgrid economic dispatching model provided in step S301 provided in the embodiment of the present invention, the power generation cost and the environmental governance cost are used as the objective functions, the power constraint is used as the constraint condition, and a dispatching strategy for preferential output of the uncontrollable microgrid is provided.
Example 3
As shown in fig. 1 and fig. 5, as a preferred embodiment, the method for diagnosing a fault of a microgrid through a fault diagnosis circuit according to an embodiment of the present invention includes:
s401, constructing a power grid full model.
S402, obtaining fault information of the feeder line, and positioning the feeder line where the fault is located according to the fault information and the power grid full model.
And S403, acquiring fault indication information of the fault indicator, and positioning a first fault section where the fault is located according to the fault indication information and the power grid full model.
S404, acquiring a distribution and transformation power failure event of the distribution transformer, and positioning first tripping equipment where the fault is located according to the distribution and transformation power failure event and the power grid full model.
S405, obtaining measurement sudden drop information of the outgoing line switch, and positioning a second fault interval and second tripping equipment where the fault is located according to the measurement sudden drop information and the power grid full model.
S406, generating a diagnosis report according to the feeder line, the first fault interval, the first trip device, the second fault interval and the second trip device which are obtained through positioning.
And S407, outputting fault comprehensive information and a diagnosis report, wherein the fault comprehensive information comprises the first fault interval, the first trip equipment, the second fault interval and the second trip equipment.
The fault indication information provided by the embodiment of the invention comprises card turning action information, a fault current value and a load current value;
the diagnosis processing on the corresponding feeder line specifically includes:
acquiring the current value of the distribution transformer of the corresponding feeder line;
positioning a first tripping device where a fault is located according to the current value of the distribution transformer and the full power grid model
The method for acquiring the power distribution outage event in step S404 provided by the embodiment of the present invention specifically includes:
detecting the distribution transformer blackout event of the distribution transformer;
when the distribution transformer power failure event is triggered, acquiring the distribution transformer power failure event of the distribution transformer;
and triggering diagnosis processing of the corresponding feeder line according to the power distribution outage event.
The method for acquiring the measurement dip information in step S405 provided by the embodiment of the present invention is as follows:
obtaining the measurement sudden drop proportion of the outgoing line switches;
detecting whether the measurement sudden drop proportion is larger than a preset threshold value or not, and positioning a second fault interval and second tripping equipment according to the outgoing line switch and the power grid full model when the measurement sudden drop proportion of the outgoing line switch is larger than the preset threshold value;
and when the measurement sudden-drop ratio of a certain outgoing switch is detected to be larger than a preset threshold value, obtaining the measurement information of the two-remote and three-remote switches, the distribution transformer and the fault indicator on the feeder line corresponding to the outgoing switch, and positioning a second fault interval and second tripping equipment according to the measurement information of the two-remote and three-remote switches, the distribution transformer and the fault indicator and the full model of the power grid.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. The optimal scheduling method for the microgrid is characterized by comprising the following steps of:
step one, optimizing the configuration of the microgrid according to configured microgrid parameters through a power grid optimization program: (I) acquiring a power sequence and a load sequence of a wind driven generator and a solar photovoltaic cell for supplying power to a microgrid through power detection equipment;
(II) establishing an island microgrid capacity optimization configuration model by taking the comprehensive investment cost, the comprehensive reliability, the residual energy rate and the renewable energy source rate as planning targets;
(III) solving the capacity optimization configuration model of the island microgrid by adopting an improved hybrid particle swarm algorithm based on the step (I) to obtain an optimal configuration scheme;
step two, scheduling the optimized micro-grid power supply through a power grid scheduling program: (1) constructing a micro-grid optimization mathematical model, giving a calculation mode of grid output power, starting cost and operation and maintenance cost through the mathematical model, and giving grid power generation cost and environmental management cost; the distributed power supply comprises an uncontrollable micro power supply and a controllable micro power supply;
(2) the method comprises the steps that a microgrid economic dispatching model is built through a mathematical model, a dispatching optimization strategy which preferentially utilizes all generated energy of an uncontrollable microgrid and a diesel generator as a standby power supply is adopted by taking the minimum power generation cost and the minimum environmental management cost as objective functions, and electric energy interaction is carried out with a large power grid only when the microgrid cannot meet the load requirement or the microgrid has excessive power;
(3) adjusting the state of the particles by self experience and group experience through a particle swarm optimization algorithm to optimize system parameters, and constructing an improved particle swarm optimization algorithm; the initial positions of the particles in the particle swarm are randomly generated in the search area, and the speed of each particle is randomly given; applying a particle swarm algorithm to a program, wherein each particle moves at a solution space position once, and then optimizing is repeatedly performed after one iterative process is completed until one of the following conditions is met: the particles are relatively static in the solution space, or the maximum iteration number is reached;
step three, diagnosing the micro-grid fault through a fault diagnosis circuit:
1) constructing a power grid full model;
2) acquiring fault information of a feeder line, and positioning the feeder line where the fault is located according to the fault information and the power grid full model;
3) acquiring fault indication information of a fault indicator, and positioning a first fault section where a fault is located according to the fault indication information and the power grid full model;
4) acquiring a distribution transformer power failure event of a distribution transformer, and positioning first tripping equipment where a fault is located according to the distribution transformer power failure event and the power grid full model;
5) acquiring measurement sudden drop information of an outgoing line switch, and positioning a second fault interval and second tripping equipment where a fault is located according to the measurement sudden drop information and the power grid full model;
6) generating a diagnosis report according to the feeder line, the first fault interval, the first trip device, the second fault interval and the second trip device obtained by positioning;
7) and outputting fault comprehensive information and a diagnosis report, wherein the fault comprehensive information comprises the first fault interval, the first trip equipment, the second fault interval and the second trip equipment.
2. The microgrid optimized scheduling method of claim 1, wherein step one is preceded by: step I, detecting the power supply current data of the microgrid through an ammeter;
step II, detecting the power supply voltage data of the microgrid through a voltmeter;
step III, carrying out parameter configuration on the microgrid through a configuration program according to the detected current and voltage supplied by the microgrid;
after the third step, the following steps are required:
step 1, processing microgrid data by using cloud computing through a cloud server;
step 2, storing the current, the voltage, the configuration data and the real-time data of the fault diagnosis result of the microgrid through a memory;
and 3, displaying the detected current, voltage and configuration data of the microgrid and real-time data of the fault diagnosis result through a display.
3. The microgrid optimization scheduling method of claim 1, wherein in the first step, the island microgrid capacity optimization configuration model of the step (III) is as follows:
minF(x)＝w1C+w3ηLPSP+w2fEER+W4fre
s.t.SOCmin≤SOC(t)≤SOCmax
Pi，min≤Pi(t)≤Pi，max
Ebs，rest≥ηEgen，rest
ηLPSP≥ηLPSP，min
fre≥fre，min；
wherein C is the total annual average cost of the micro-grid investment ηLPSP and ηLPSP，minRespectively, the comprehensive reliability and the lower limit of the comprehensive reliability; f. ofEERIs the remaining energy rate; f. ofreAnd fre，minRespectively, the renewable energy rate and the renewable energy rate lower limit; SOC (t), SOCmin、SOCmaxThe state of the charge quantity of the storage battery and the minimum value and the maximum value of the charge quantity are respectively; pi (t), Pi，min、Pi，maxRespectively the output power of the micro power supply and the lower limit and the upper limit of the output power; ebs，restIs the remaining chargeable quantity of the storage battery; egen，restThe surplus of the electric energy required by the load is subtracted from the generated electric energy, η is the charging efficiency of the storage battery, w1、w2、w3、w4The weight coefficients of the targets are respectively; x is a decision variable.
4. The microgrid optimization scheduling method of claim 1, wherein in the second step, the microgrid economic scheduling model proposed in the step (1) takes power generation cost and environmental governance cost as objective functions, takes power constraints as constraints and proposes a scheduling strategy of preferential output of the uncontrollable microgrid.
5. The microgrid optimization scheduling method of claim 1, wherein in step three, the fault indication information comprises card flipping action information, a fault current value and a load current value;
the diagnosis processing on the corresponding feeder line specifically includes:
acquiring the current value of the distribution transformer of the corresponding feeder line;
and positioning the first tripping equipment where the fault is located according to the current value of the distribution transformer and the full power grid model.
6. The microgrid optimization scheduling method according to claim 1, wherein in step three, the method for acquiring the power distribution outage event in step 4) specifically comprises:
detecting the distribution transformer blackout event of the distribution transformer;
when the distribution transformer power failure event is triggered, acquiring the distribution transformer power failure event of the distribution transformer;
and triggering diagnosis processing of the corresponding feeder line according to the power distribution outage event.
7. The microgrid optimization scheduling method according to claim 1, wherein in step three, the method for obtaining measurement dip information in step 5) is as follows:
obtaining the measurement sudden drop proportion of the outgoing line switches;
detecting whether the measurement sudden drop proportion is larger than a preset threshold value or not, and positioning a second fault interval and second tripping equipment according to the outgoing line switch and the power grid full model when the measurement sudden drop proportion of the outgoing line switch is larger than the preset threshold value;
and when the measurement sudden-drop ratio of a certain outgoing switch is detected to be larger than a preset threshold value, obtaining the measurement information of the two-remote and three-remote switches, the distribution transformer and the fault indicator on the feeder line corresponding to the outgoing switch, and positioning a second fault interval and second tripping equipment according to the measurement information of the two-remote and three-remote switches, the distribution transformer and the fault indicator and the full model of the power grid.
8. A microgrid optimized dispatching system applying the microgrid optimized dispatching method as claimed in any one of claims 1 to 7, characterized in that the microgrid optimized dispatching system comprises:
the current detection module is connected with the main control module and used for detecting the power supply current data of the micro-grid through the ammeter;
the voltage detection module is connected with the main control module and used for detecting the power supply voltage data of the micro-grid through a voltmeter;
the power grid parameter configuration module is connected with the main control module and used for carrying out parameter configuration on the micro-grid according to the detected current and voltage supplied by the micro-grid through a configuration program;
the main control module is connected with the current detection module, the voltage detection module, the power grid parameter configuration module, the power grid optimization module, the power grid scheduling module, the power grid fault diagnosis module, the cloud processing module, the data storage module and the display module and is used for controlling each module to normally work through the main control computer;
the power grid optimization module is connected with the main control module and used for optimizing the configuration of the micro-grid according to the configured micro-grid parameters through a power grid optimization program;
the power grid dispatching module is connected with the main control module and used for dispatching the optimized micro-grid power supply through a power grid dispatching program;
the power grid fault diagnosis module is connected with the main control module and used for diagnosing the micro-grid fault through the fault diagnosis circuit;
the cloud processing module is connected with the main control module and used for processing the microgrid data by utilizing cloud computing through the cloud server;
the data storage module is connected with the main control module and used for storing the current, the voltage, the configuration data and the real-time data of the fault diagnosis result of the microgrid through the memory;
and the display module is connected with the main control module and used for displaying the detected current, voltage, configuration data and real-time data of the fault diagnosis result of the microgrid through the display.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for implementing the microgrid optimized scheduling method of any of claims 1-7 when executed on an electronic device.
10. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the microgrid optimized scheduling method of any one of claims 1-7.
CN202010247352.2A 2020-03-31 2020-03-31 Microgrid optimization scheduling system, method, storage medium and computer program Pending CN111342462A (en)

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