CN113078685A - Micro-grid dynamic partitioning method and system - Google Patents

Micro-grid dynamic partitioning method and system Download PDF

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CN113078685A
CN113078685A CN202110243461.1A CN202110243461A CN113078685A CN 113078685 A CN113078685 A CN 113078685A CN 202110243461 A CN202110243461 A CN 202110243461A CN 113078685 A CN113078685 A CN 113078685A
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microgrid
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CN113078685B (en
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景巍巍
徐晓春
王栋
丁波
范广博
杨东升
顾雪楠
王毅
闪鑫
马晨霄
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NARI Nanjing Control System Co Ltd
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NARI Group Corp
Nari Technology Co Ltd
HuaiAn Power Supply Co of State Grid Jiangsu 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
    • 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
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in 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
    • 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
    • H02J3/388—Islanding, i.e. disconnection of local power supply from the network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • 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]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention discloses a micro-grid dynamic partitioning method which comprises the steps of collecting power grid topological data, and partitioning an initial range of a micro-grid based on a principle of power balance in the micro-grid and least load removal; according to the initial range of the microgrid, constructing a microgrid dynamic optimization model by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets; and solving the microgrid dynamic optimization model to obtain an optimal microgrid range. A corresponding system is also disclosed. According to the method, the initial range of the microgrid is divided according to the topological data of the power grid, a dynamic optimization model of the microgrid is constructed by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets, and the dynamic division of the microgrid is effectively realized based on the model.

Description

Micro-grid dynamic partitioning method and system
Technical Field
The invention relates to a dynamic partitioning method and a dynamic partitioning system for a micro-grid, and belongs to the field of dispatching automation optimization dispatching.
Background
With the continuous development of new energy technology, distributed power supplies such as photovoltaic power, wind power, energy storage and gas turbine are connected in large quantities, regional power grids are becoming a new generation of comprehensive energy internet with multi-source connected source grid charge storage coordinated operation, local regions already have micro-grid operation characteristics, and can independently operate under the condition of losing system power supplies. At present, a regional power grid has proposed a main grid and microgrid integrated operation mode, which requires that the main grid and the microgrid can independently operate in normal and emergency states (such as a planned maintenance or fault mode), and challenges are provided for the regulation and control operation of the regional power grid, and dynamic division of the regional power grid microgrid needs to be realized.
Disclosure of Invention
The invention provides a micro-grid dynamic partitioning method and system, and solves the problems disclosed in the background technology.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a micro-grid dynamic partitioning method comprises the following steps,
collecting power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance in the microgrid and least load removal;
according to the initial range of the microgrid, constructing a microgrid dynamic optimization model by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets;
and solving the microgrid dynamic optimization model to obtain an optimal microgrid range.
Collecting topological data of a power grid, dividing the initial range of the microgrid based on the principle of power balance and minimum load removal in the microgrid, and specifically comprising the following steps of,
collecting power grid topology data;
based on the power grid topological data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight connected with each bus node into an edge set;
based on the power grid topological data, storing all distributed power source nodes into a node set, storing edges connected with all the distributed power source nodes into an edge set, and calculating the sum of power sent by all the distributed power sources;
sequentially superposing load power according to the principle that the edge weight is from small to large, and respectively placing edges and nodes corresponding to the load into an edge set and a node set until the superposed load power is not less than the sum of power sent by all distributed power supplies;
and obtaining the micro-grid initial range according to the edge set and the node set.
The micro-grid dynamic optimization model is as follows,
an objective function:
Figure BDA0002963170310000021
wherein F is an objective function; n is a radical ofTTo optimize the periodThe number of divided time segments; n is a radical ofpIs the total number of nodes, U, of the power gridi(t) is the ith grid node voltage; CB is the total number of switches; n is a radical oflineThe number of times of adjustment for the tie line;
Figure BDA0002963170310000022
adjusting the states of the front and rear switches for the tie lines respectively; n is a radical ofqThe number of the index items is the running number of the microgrid; lambda [ alpha ]qMembership function of the q-th operation index;
and power balance constraint:
∑PTRANS(t)+∑PGEN(t)-∑PLOAD(t)＝0
wherein, PTRANS(t)、PGEN(t)、PLOAD(t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
SSOCmin≤SSOC(t)≤SSOCmax
wherein S isSOC(t) is the energy storage state of charge; sSOCmax、SSOCminRespectively an upper limit and a lower limit of the energy storage charge state;
energy storage charge and discharge power constraint:
PBESSin(t)≤PBESSin,max
PBESSout(t)≤PBESSout,max
wherein, PBESSin,max、PBESSout,maxRespectively the maximum charging and discharging power of the stored energy; pBESSin(t) charging power for storing energy; pBESSout(t) is the discharge power of the stored energy;
and (3) line margin constraint:
Pline,min≤Pline(t)≤Pline,max
Qline,min≤Qline(t)≤Qline,max
Iline,min≤Iline(t)≤Iline,max
wherein the content of the first and second substances,Pline(t)、Qline(t)、Iline(t) active, reactive and current on the line respectively; pline,max、Pline,minAre respectively Pline(t) upper and lower limits; qline,max、Qline,minAre respectively Qline(t) upper and lower limits; i isline,max、Iline,minAre respectively Iline(t) upper and lower limits.
And solving the microgrid dynamic optimization model by adopting a mixed integer linear programming method to obtain an optimal microgrid range.
A micro-grid dynamic partitioning system comprises,
an initial dividing module: collecting power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance in the microgrid and least load removal;
a model construction module: according to the initial range of the microgrid, constructing a microgrid dynamic optimization model by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets;
an optimal acquisition module: and solving the microgrid dynamic optimization model to obtain an optimal microgrid range.
The initial partitioning module includes a first partition module for partitioning the image data,
the acquisition module is used for: collecting power grid topology data;
the first search module: based on the power grid topological data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight connected with each bus node into an edge set;
a power sum calculation module: based on the power grid topological data, storing all distributed power source nodes into a node set, storing edges connected with all the distributed power source nodes into an edge set, and calculating the sum of power sent by all the distributed power sources;
a second search module: sequentially superposing load power according to the principle that the edge weight is from small to large, and respectively placing edges corresponding to loads and bus nodes into an edge set and a node set until the superposed load power is not less than the sum of powers sent by all distributed power supplies;
a result module: and obtaining the micro-grid initial range according to the edge set and the node set.
The micro-grid dynamic optimization model constructed by the model construction module comprises the following steps,
an objective function:
Figure BDA0002963170310000041
wherein F is an objective function; n is a radical ofTOptimizing the number of time segments divided in the period; n is a radical ofpIs the total number of nodes, U, of the power gridi(t) is the ith grid node voltage; CB is the total number of switches; n is a radical oflineThe number of times of adjustment for the tie line;
Figure BDA0002963170310000042
adjusting the states of the front and rear switches for the tie lines respectively; n is a radical ofqThe number of the index items is the running number of the microgrid; lambda [ alpha ]qMembership function of the q-th operation index;
and power balance constraint:
∑PTRANS(t)+∑PGEN(t)-∑PLOAD(t)＝0
wherein, PTRANS(t)、PGEN(t)、PLOAD(t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
SSOCmin≤SSOC(t)≤SSOCmax
wherein S isSOC(t) is the energy storage state of charge; sSOCmax、SSOCminRespectively an upper limit and a lower limit of the energy storage charge state;
energy storage charge and discharge power constraint:
PBESSin(t)≤PBESSin,max
PBESSout(t)≤PBESSout,max
wherein, PBESSin,max、PBESSout,maxRespectively the maximum charging and discharging power of the stored energy; pBESSin(t) charging power for storing energy; pBESSout(t) is the discharge power of the stored energy;
and (3) line margin constraint:
Pline,min≤Pline(t)≤Pline,max
Qline,min≤Qline(t)≤Qline,max
Iline,min≤Iline(t)≤Iline,max
wherein, Pline(t)、Qline(t)、Iline(t) active, reactive and current on the line respectively; pline,max、Pline,minAre respectively Pline(t) upper and lower limits; qline,max、Qline,minAre respectively Qline(t) upper and lower limits; i isline,max、Iline,minAre respectively Iline(t) upper and lower limits.
And the optimal acquisition module is used for solving the micro-grid dynamic optimization model by adopting a mixed integer linear programming method to acquire an optimal micro-grid range.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a microgrid dynamic partitioning method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing a microgrid dynamic partitioning method.
The invention achieves the following beneficial effects: 1. according to the method, the initial range of the microgrid is divided according to the topological data of the power grid, a microgrid dynamic optimization model is constructed by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets, and the dynamic division of the microgrid is effectively realized on the basis of the model; 2. the microgrid dynamic optimization model aims at minimizing voltage deviation, minimizing switching operation times and optimizing operation index evaluation, improves the voltage quality of the microgrid, reduces switching wear, prolongs the sustainable operation time of the microgrid, improves the economy and improves the power supply reliability of the main network and the microgrid.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a method for dynamically partitioning a microgrid includes the following steps:
step 1, collecting power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance and minimum load removal in the microgrid.
The specific process is as follows:
11) collecting power grid topological data and setting the weight of the edge;
12) based on the power grid topological data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight connected with each bus node into an edge set;
13) based on the power grid topological data, storing all distributed power source nodes into a node set, storing edges connected with all the distributed power source nodes into an edge set, and calculating the sum of power sent by all the distributed power sources;
14) sequentially superposing load power according to the principle that the edge weight is from small to large, and respectively placing edges and nodes corresponding to the load into an edge set and a node set until the superposed load power is not less than the sum of power sent by all distributed power supplies;
15) acquiring a micro-grid initial range according to the edge set and the node set;
16) and checking the initial range of the microgrid, and checking whether the wireless circuit is overloaded and whether the bus voltage is out of limit.
And 2, comprehensively considering power grid safety constraint and resource adjustability constraint according to the initial range of the microgrid, and constructing a microgrid dynamic optimization model by taking minimum voltage deviation, minimum switching operation times and optimal operation index evaluation as targets.
The micro-grid dynamic optimization model is as follows:
an objective function:
Figure BDA0002963170310000071
wherein F is an objective function; n is a radical ofTIn order to optimize the number of time segments divided in the cycle, the output force, the energy storage output force and the load of each distributed power supply can be considered to be unchanged for each time segment; n is a radical ofpIs the total number of nodes, U, of the power gridi(t) is the ith grid node voltage; CB is the total number of switches; n is a radical oflineThe number of times of adjustment for the tie line;
Figure BDA0002963170310000072
adjusting the states of the front and rear switches for the tie lines respectively; n is a radical ofqThe number of the index items is the running number of the microgrid; lambda [ alpha ]qIs a membership function of the q-th operation index with membership of [0, 1%]The larger the membership degree is, the better the index is, the better the microgrid operation performance is, and the longer the microgrid sustainable operation time is.
Constraint conditions are as follows:
and power balance constraint:
∑PTRANS(t)+∑PGEN(t)-∑PLOAD(t)＝0
wherein, PTRANS(t)、PGEN(t)、PLOAD(t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
SSOCmin≤SSOC(t)≤SSOCmax
wherein S isSOC(t) is the energy storage state of charge; sSOCmax、SSOCminRespectively an upper limit and a lower limit of the energy storage charge state;
energy storage charge and discharge power constraint:
PBESSin(t)≤PBESSin,max
PBESSout(t)≤PBESSout,max
wherein, PBESSin,max、PBESSout,maxThe maximum charging and discharging power of the stored energy is generally related to the total capacity of the stored energy; pBESSin(t) charging power for storing energy; pBESSout(t) is the discharge power of the stored energy;
and (3) line margin constraint:
Pline,min≤Pline(t)≤Pline,max
Qline,min≤Qline(t)≤Qline,max
Iline,min≤Iline(t)≤Iline,max
wherein, Pline(t)、Qline(t)、Iline(t) active, reactive and current on the line respectively; pline,max、Pline,minAre respectively Pline(t) upper and lower limits; qline,max、Qline,minAre respectively Qline(t) upper and lower limits; i isline,max、Iline,minAre respectively Iline(t) upper and lower limits.
And 3, solving the microgrid dynamic optimization model by adopting a mixed integer linear programming method to obtain an optimal microgrid range.
A nonlinear term exists in an objective function of the microgrid dynamic optimization model, and a binary variable alpha with a value of 0 or 1 is introducediRemove t period | UiAbsolute value sign of 1 |:
|Ui-1|＝(2αi-1)(Ui-1)
wherein alpha isiTo indicate UiBinary variable of 1 symbol, when Ui1 is not negative, then αiIs 1, otherwise, when Ui1 is negative, then αiIs 0.
The above relationship can be expressed linearly as:
Figure BDA0002963170310000091
Figure BDA0002963170310000092
Figure BDA0002963170310000093
in which continuous variables are used
Figure BDA0002963170310000094
Replace alphai(Ui-1) when α isiWhen the average molecular weight is 0, the average molecular weight,
Figure BDA0002963170310000095
is 0 and has Ui-1 is negative wheniWhen the number of the carbon atoms is 1,
Figure BDA0002963170310000096
and U isi-1 is not negative, M is constantly greater than Ui-a constant of 1.
The second part of the objective function carries out linearization conversion similarly:
Figure BDA0002963170310000097
Figure BDA0002963170310000098
Figure BDA0002963170310000099
the mixed integer linear programming method ensures the global optimality of the obtained solution.
According to the method, the initial range of the microgrid is divided according to the topological data of the power grid, a microgrid dynamic optimization model is constructed by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets, and the dynamic division of the microgrid is effectively realized on the basis of the model; meanwhile, the microgrid dynamic optimization model aims at minimizing voltage deviation, minimizing switching operation times and optimizing operation index evaluation, improves the voltage quality of the microgrid, reduces switching wear, prolongs the sustainable operation time of the microgrid, improves the economy and improves the power supply reliability of the main network and the microgrid.
A micro-grid dynamic partitioning system comprises,
an initial dividing module: and collecting power grid topological data, and dividing the initial range of the microgrid based on the principle of power balance and minimum load removal in the microgrid.
The initial partitioning module includes a first partition module for partitioning the image data,
the acquisition module is used for: collecting power grid topology data;
the first search module: based on the power grid topological data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight connected with each bus node into an edge set;
a power sum calculation module: based on the power grid topological data, storing all distributed power source nodes into a node set, storing edges connected with all the distributed power source nodes into an edge set, and calculating the sum of power sent by all the distributed power sources;
a second search module: sequentially superposing load power according to the principle that the edge weight is from small to large, and respectively placing edges corresponding to loads and bus nodes into an edge set and a node set until the superposed load power is not less than the sum of powers sent by all distributed power supplies;
a result module: and obtaining the micro-grid initial range according to the edge set and the node set.
A model construction module: and constructing a microgrid dynamic optimization model according to the initial range of the microgrid by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets.
The micro-grid dynamic optimization model constructed by the model construction module comprises the following steps,
an objective function:
Figure BDA0002963170310000101
wherein F is an objective function; n is a radical ofTOptimizing the number of time segments divided in the period; n is a radical ofpIs the total number of nodes, U, of the power gridi(t) is the ith grid node voltage; CB is the total number of switches; n is a radical oflineThe number of times of adjustment for the tie line;
Figure BDA0002963170310000111
adjusting the states of the front and rear switches for the tie lines respectively; n is a radical ofqThe number of the index items is the running number of the microgrid; lambda [ alpha ]qMembership function of the q-th operation index;
and power balance constraint:
∑PTRANS(t)+∑PGEN(t)-∑PLOAD(t)＝0
wherein, PTRANS(t)、PGEN(t)、PLOAD(t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
SSOCmin≤SSOC(t)≤SSOCmax
wherein S isSOC(t) is the energy storage state of charge; sSOCmax、SSOCminRespectively an upper limit and a lower limit of the energy storage charge state;
energy storage charge and discharge power constraint:
PBESSin(t)≤PBESSin,max
PBESSout(t)≤PBESSout,max
wherein, PBESSin,max、PBESSout,maxRespectively the maximum charging and discharging power of the stored energy; pBESSin(t) charging power for storing energy; pBESSout(t) is the discharge power of the stored energy;
and (3) line margin constraint:
Pline,min≤Pline(t)≤Pline,max
Qline,min≤Qline(t)≤Qline,max
Iline,min≤Iline(t)≤Iline,max
wherein, Pline(t)、Qline(t)、Iline(t) active, reactive and current on the line respectively; pline,max、Pline,minAre respectively Pline(t) upper and lower limits; qline,max、Qline,minAre respectively Qline(t) upper and lower limits; i isline,max、Iline,minAre respectively Iline(t) upper and lower limits.
And the optimal acquisition module is used for solving the micro-grid dynamic optimization model by adopting a mixed integer linear programming method to acquire an optimal micro-grid range.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a microgrid dynamic partitioning method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing a microgrid dynamic partitioning method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (10)

1. A micro-grid dynamic partitioning method is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
collecting power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance in the microgrid and least load removal;
according to the initial range of the microgrid, constructing a microgrid dynamic optimization model by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets;
and solving the microgrid dynamic optimization model to obtain an optimal microgrid range.
2. The dynamic partitioning method for the microgrid according to claim 1, characterized in that: collecting topological data of a power grid, dividing the initial range of the microgrid based on the principle of power balance and minimum load removal in the microgrid, and specifically comprising the following steps of,
collecting power grid topology data;
based on the power grid topological data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight connected with each bus node into an edge set;
based on the power grid topological data, storing all distributed power source nodes into a node set, storing edges connected with all the distributed power source nodes into an edge set, and calculating the sum of power sent by all the distributed power sources;
sequentially superposing load power according to the principle that the edge weight is from small to large, and respectively placing edges and nodes corresponding to the load into an edge set and a node set until the superposed load power is not less than the sum of power sent by all distributed power supplies;
and obtaining the micro-grid initial range according to the edge set and the node set.
3. The dynamic partitioning method for the microgrid according to claim 1, characterized in that: the micro-grid dynamic optimization model is as follows,
an objective function:
Figure FDA0002963170300000021
wherein F is an objective function; n is a radical ofTOptimizing the number of time segments divided in the period; n is a radical ofpIs the total number of nodes, U, of the power gridi(t) is the ith grid node voltage; CB is the total number of switches; n is a radical oflineThe number of times of adjustment for the tie line;
Figure FDA0002963170300000022
adjusting the shape of the front and rear switches for the tie-line respectivelyState; n is a radical ofqThe number of the index items is the running number of the microgrid; lambda [ alpha ]qMembership function of the q-th operation index;
and power balance constraint:
∑PTRANS(t)+∑PGEN(t)-∑PLOAD(t)＝0
wherein, PTRANS(t)、PGEN(t)、PLOAD(t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
SSOCmin≤SSOC(t)≤SSOCmax
wherein S isSOC(t) is the energy storage state of charge; sSOCmax、SSOCminRespectively an upper limit and a lower limit of the energy storage charge state;
energy storage charge and discharge power constraint:
PBESSin(t)≤PBESSin,max
PBESSout(t)≤PBESSout,max
wherein, PBESSin,max、PBESSout,maxRespectively the maximum charging and discharging power of the stored energy; pBESSin(t) charging power for storing energy; pBESSout(t) is the discharge power of the stored energy;
and (3) line margin constraint:
Pline,min≤Pline(t)≤Pline,max
Qline,min≤Qline(t)≤Qline,max
Iline,min≤Iline(t)≤Iline,max
wherein, Pline(t)、Qline(t)、Iline(t) active, reactive and current on the line respectively; pline,max、Pline,minAre respectively Pline(t) upper and lower limits; qline,max、Qline,minAre respectively Qline(t) upper and lower limits; i isline,max、Iline,minAre respectively Iline(t) upper and lower limits.
4. The dynamic partitioning method for the microgrid according to claim 1, characterized in that: and solving the microgrid dynamic optimization model by adopting a mixed integer linear programming method to obtain an optimal microgrid range.
5. A micro-grid dynamic partitioning system is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
an initial dividing module: collecting power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance in the microgrid and least load removal;
a model construction module: according to the initial range of the microgrid, constructing a microgrid dynamic optimization model by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets;
an optimal acquisition module: and solving the microgrid dynamic optimization model to obtain an optimal microgrid range.
6. The microgrid dynamic partitioning system of claim 5, wherein: the initial partitioning module includes a first partition module for partitioning the image data,
the acquisition module is used for: collecting power grid topology data;
the first search module: based on the power grid topological data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight connected with each bus node into an edge set;
a power sum calculation module: based on the power grid topological data, storing all distributed power source nodes into a node set, storing edges connected with all the distributed power source nodes into an edge set, and calculating the sum of power sent by all the distributed power sources;
a second search module: sequentially superposing load power according to the principle that the edge weight is from small to large, and respectively placing edges corresponding to loads and bus nodes into an edge set and a node set until the superposed load power is not less than the sum of powers sent by all distributed power supplies;
a result module: and obtaining the micro-grid initial range according to the edge set and the node set.
7. The microgrid dynamic partitioning system of claim 5, wherein: the micro-grid dynamic optimization model constructed by the model construction module comprises the following steps,
an objective function:
Figure FDA0002963170300000041
wherein F is an objective function; n is a radical ofTOptimizing the number of time segments divided in the period; n is a radical ofpIs the total number of nodes, U, of the power gridi(t) is the ith grid node voltage; CB is the total number of switches; n is a radical oflineThe number of times of adjustment for the tie line;
Figure FDA0002963170300000042
adjusting the states of the front and rear switches for the tie lines respectively; n is a radical ofqThe number of the index items is the running number of the microgrid; lambda [ alpha ]qMembership function of the q-th operation index;
and power balance constraint:
∑PTRANS(t)+∑PGEN(t)-∑PLOAD(t)＝0
wherein, PTRANS(t)、PGEN(t)、PLOAD(t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
SSOCmin≤SSOC(t)≤SSOCmax
wherein S isSOC(t) is the energy storage state of charge; sSOCmax、SSOCminRespectively an upper limit and a lower limit of the energy storage charge state;
energy storage charge and discharge power constraint:
PBESSin(t)≤PBESSin,max
PBESSout(t)≤PBESSout,max
wherein, PBESSin,max、PBESSout,maxRespectively maximum of stored energyCharging and discharging power; pBESSin(t) charging power for storing energy; pBESSout(t) is the discharge power of the stored energy;
and (3) line margin constraint:
Pline,min≤Pline(t)≤Pline,max
Qline,min≤Qline(t)≤Qline,max
Iline,min≤Iline(t)≤Iline,max
wherein, Pline(t)、Qline(t)、Iline(t) active, reactive and current on the line respectively; pline,max、Pline,minAre respectively Pline(t) upper and lower limits; qline,max、Qline,minAre respectively Qline(t) upper and lower limits; i isline,max、Iline,minAre respectively Iline(t) upper and lower limits.
8. The microgrid dynamic partitioning system of claim 5, wherein: and the optimal acquisition module is used for solving the micro-grid dynamic optimization model by adopting a mixed integer linear programming method to acquire an optimal micro-grid range.
9. A computer readable storage medium storing one or more programs, characterized in that: the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1-4.
10. A computing device, characterized by: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-4.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106602557A (en) * 2017-02-24 2017-04-26 三峡大学 Multi-period optimization reconstruction method of active power distribution network comprising electric automobiles
CN107546773A (en) * 2017-09-25 2018-01-05 天津大学 A kind of more micro-capacitance sensor dynamic networking methods in region based on graph theory

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106602557A (en) * 2017-02-24 2017-04-26 三峡大学 Multi-period optimization reconstruction method of active power distribution network comprising electric automobiles
CN107546773A (en) * 2017-09-25 2018-01-05 天津大学 A kind of more micro-capacitance sensor dynamic networking methods in region based on graph theory

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