CN111952964A - Decoupling method of multi-period fault recovery model of power distribution network - Google Patents

Decoupling method of multi-period fault recovery model of power distribution network Download PDF

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CN111952964A
CN111952964A CN202010761329.5A CN202010761329A CN111952964A CN 111952964 A CN111952964 A CN 111952964A CN 202010761329 A CN202010761329 A CN 202010761329A CN 111952964 A CN111952964 A CN 111952964A
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load
power
period
distribution network
state
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刘家妤
许寅
冯昱尧
郭强
王颖
王维
杜嫣然
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Beijing Jiaotong University
State Grid Shanghai Electric Power Co Ltd
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State Grid Shanghai 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
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • 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]
    • 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
    • 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

Abstract

The invention provides a decoupling method of a multi-period fault recovery model of a power distribution network, which comprises the following two steps: neglecting voltage constraint, approximating each node voltage as rated voltage, determining a network loss factor a representing the network loss level according to the load level of the network in a normal operation state, calculating the load as (1+ a) times of the original capacity, establishing and solving a multi-period MILP model, and determining the load state and the topology state; under the condition that the integer variable value is determined, a multi-period SOCP model is established, and the output of the generator and the power flow data in the model are solved. The decoupling method provided by the invention can shorten the solving time of a Mixed Integer Second Order Cone Programming (MISOCP) model of a multi-period fault recovery problem, realize the rapid generation of a recovery strategy and simultaneously ensure the solving precision.

Description

Decoupling method of multi-period fault recovery model of power distribution network
Technical Field
The invention relates to the technical field of multi-period fault recovery of a power distribution network, in particular to a decoupling method of a multi-period fault recovery model of the power distribution network.
Background
Blackout accidents caused by extreme events increase year by year, and the toughness of the power system needs to be improved. The multi-source collaborative power distribution network recovery method is researched, the supporting effect of various distributed power supplies on power distribution network recovery is brought into full play, the power failure loss is reduced, the feasible fault recovery strategy is formulated, the power failure time of key loads can be effectively shortened, the normal operation of key infrastructure is guaranteed, and the toughness of the power distribution network for dealing with extreme events is improved.
Extreme events may destroy a large amount of power infrastructure in a short time, causing a blackout accident, and due to limited power generation resources, under extreme disaster conditions, there may be a case where power cannot be supplied to important loads during a full outage period. The task of recovering the critical load is to determine a load set to be recovered, a power output value and a recovery path, namely the state of each line switch, on the premise of meeting all constraints, so as to supply power to the critical load as much as possible and more durably. In the multi-source cooperative recovery strategy, a microgrid, a distributed power supply/energy storage and a load can be connected through operations of a power switch, a section switch and the like, so that a single or a plurality of electrical islands are formed. LIU Weijia, SUN Lei, LIN Zhenzhi and the like consider the uncertainty of the output power of the intermittent energy source, provide a short-time recovery power supply strategy containing different types of power supply systems of the intermittent energy source, the stored energy, the electric automobile and the like, and provide a time point for closing a switch after a fault occurs. According to the multi-time-step recovery method for recovering by using the distributed power supply after an extreme accident, the CHEN Bo, the CHEN Chen, the WANG Jianhui and the like propose a multi-time-step recovery method, however, the model is subjected to linearization processing when the constraint of a power flow equation is considered, the network loss is not considered, and the result is not accurate enough. XU Yin, WANG Ying, HE Jinghan and the like establish a mixed integer second-order cone planning problem of a recovery problem, can accurately model a load flow equation, and solve the load flow equation into an accurate solution, but a mature commercial optimization solver is used for solving the load flow equation, so that the problems of low convergence speed and overlong calculation time exist, and the online application requirements cannot be met. At present, a solving method for a mixed integer second-order cone planning model considering the multi-source collaborative multi-period fault recovery problem of a power distribution network is limited, and the solving time is long or the solving is difficult.
Disclosure of Invention
The embodiment of the invention provides a decoupling method for a multi-period fault recovery model of a power distribution network, and aims to solve the problems that the existing method for solving a multi-period mixed integer second-order cone programming Model (MISOCP) problem has too many iteration times and long solving time and cannot meet the requirements of online application.
In order to achieve the purpose, the invention adopts the following technical scheme.
A decoupling method of a multi-period fault recovery model of a power distribution network comprises the following steps:
s1, approximating the voltage of each node in the power distribution network to a rated voltage, and determining a network loss factor a for representing the network loss level of the power distribution network; calculating the load level of the power distribution network to be 1+ a times of the original capacity, establishing and solving a multi-period mixed integer linear model, and obtaining the load state and the topological state of the power distribution network;
s2, based on the load state and the topological state of the power distribution network, a multi-period second-order cone planning model is established and solved, and a power distribution network recovery scheme is obtained.
Preferably, step S1 specifically includes:
establishing an objective function of the maximum weighted load power supply time required by the distribution network in fault recovery
maxf1＝∑t∑i∈Lwiγi,t(1) (ii) a In the formula, wiIs the weight coefficient of the load; gamma rayi,tThe load i is in the state of t period, and the load is in the recovery state, gamma i,t1, otherwise 0;
adding a first constraint condition, and establishing a multi-period mixed integer linear model; the first constraint condition comprises a power flow constraint, an operation safety constraint, an energy and energy storage charge state constraint and a topology constraint;
the power flow constraint comprises the following steps:
Figure BDA0002613172150000021
Figure BDA0002613172150000027
Figure BDA0002613172150000022
Figure BDA0002613172150000023
Figure BDA0002613172150000024
Figure BDA0002613172150000025
Figure BDA0002613172150000026
in the formula, Vi,tIs a complex voltage of node I in time period t, Iij,tA complex current for line (i, j) over time period t; pki,tAnd Pij,tFor lines (k, i) and (i, j), respectively, during time period tActive power variable of Pi,tInjecting an active power variable for the node i in a time period t; qki,tAnd Qij,tReactive power variable, Q, for lines (k, i) and (i, j) respectively, during time ti,tInjecting a reactive power variable for node i at time t;
Figure BDA0002613172150000031
the active demand for the load connected to the inode during time period t,
Figure BDA0002613172150000032
the active power injected into the node for the time period t for the power supply connected to the inode,
Figure BDA0002613172150000033
the reactive demand for the load connected at the inode during time period t,
Figure BDA0002613172150000034
injecting reactive power into the node for a time period t for a power supply connected to the node i; sij,tIs the complex power variable of the line (i, j) over time period t;
operational safety constraints include:
Figure BDA0002613172150000035
Figure BDA0002613172150000036
Figure BDA0002613172150000037
in the formula (I), the compound is shown in the specification,
Figure BDA0002613172150000038
and
Figure BDA0002613172150000039
respectively representing the maximum charging power and the maximum discharging power of an energy storage or electric vehicle charging pile connected to the node i;
energy and stored energy state of charge constraints include:
Figure BDA00026131721500000310
Figure BDA00026131721500000311
in the formula, Ei,0The energy value of the power generation resource internally remaining before restoration for the power source i. K is equal to [0, 1 ]]Is the state of charge of the battery-type device,
Figure BDA00026131721500000312
and
Figure BDA00026131721500000313
respectively satisfying the upper and lower limits of the state of charge, K, of the normal operation of the equipment ii,0For initial state of charge of the apparatus, piConverting energy to a state of charge of the device for the conversion factor;
the topological constraints include:
Figure BDA00026131721500000314
Figure BDA00026131721500000315
Figure BDA00026131721500000316
r is a set formed by all root nodes, and the final sum of the number of lines is equal to the number of the nodes minus the number of the root nodes; n is the set of all nodes contained in the target island, alphaijIs a variable from 0 to 1, indicating that line (i, j) isAnd if not, continuously taking 1, and not continuously taking 0. FijRepresenting virtual branch flow, DiFor dummy load, M is a larger positive real number, and alpha is a when the lines are not connectedij＝0，Fij＝0。
Preferably, step S2 specifically includes:
establishing a multi-period network loss and minimum objective function
minPloss＝Pgen-Pload (17)；
Adding a second constraint condition, and establishing a multi-period optimal power flow model; the second constraint includes:
node complex power balance constraints
Figure BDA0002613172150000041
Figure BDA0002613172150000042
Matrix form derived from ohm's law
Figure BDA0002613172150000043
Figure BDA0002613172150000044
Second order cone constraint
Figure BDA0002613172150000045
Preferably, solving the second-order cone planning model of multiple periods of time to obtain the power distribution network recovery scheme further includes:
sequencing each node of the power distribution network according to the load grade to obtain w1>…>wn；
Adding a third constraint condition before solving the second-order cone programming model of multiple time intervals, wherein the third constraint condition comprises the following steps:
any two corresponding grades are respectively k1And k2Load i and load j, the load amounts being Pload,iAnd Pload,jWhen k is1<k2Satisfy the following requirements
Figure BDA0002613172150000046
The load of any level j is selected,
Figure BDA0002613172150000047
for the load set with the importance level of k, | · | represents the number of elements of the corresponding set, and satisfies
Figure BDA0002613172150000048
According to the technical scheme provided by the embodiment of the invention, the decoupling method for the multi-period fault recovery model of the power distribution network provided by the invention comprises the following two steps: neglecting voltage constraint, approximating each node voltage as rated voltage, determining a network loss factor a representing the network loss level according to the load level of the network in a normal operation state, calculating the load to be (1+ a) times of the original capacity, establishing and solving a multi-period Mixed Integer Linear Programming (MILP) model, and determining the load state and the topological state; under the condition that the integer variable value is determined, a multi-period second-order cone programming (SOCP) model is established, and the output and power flow data of the generator in the model are solved. The decoupling method provided by the invention can shorten the solving time of a Mixed Integer Second Order Cone Programming (MISOCP) model of a multi-period fault recovery problem, realize the rapid generation of a recovery strategy and simultaneously ensure the solving precision.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced 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 to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a processing flow chart of a decoupling method for a multi-period fault recovery model of a power distribution network according to the present invention;
FIG. 2 is a flowchart of a preferred embodiment of a decoupling method for a multi-period fault recovery model of a power distribution network according to the present invention;
FIG. 3 is a topological diagram of a test system in another embodiment of the decoupling method for a multi-period fault recovery model of a power distribution network according to the present invention;
fig. 4 is a diagram of a recovery strategy solution result in another embodiment of the decoupling method for the multi-period fault recovery model of the power distribution network provided by the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" include plural referents unless the context clearly dictates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Referring to fig. 1, the decoupling method for the multi-period fault recovery model of the power distribution network provided by the invention comprises the following steps:
s1, approximating the voltage of each node in the power distribution network to a rated voltage, and determining a network loss factor a for representing the network loss level of the power distribution network; calculating the load level of the power distribution network to be 1+ a times of the original capacity, establishing and solving a multi-period mixed integer linear model, and obtaining the load state and the topological state of the power distribution network;
s2, based on the load state and the topological state of the power distribution network, a multi-period second-order cone programming (SOCP) model is established and solved, and a power distribution network recovery scheme is obtained.
The multi-source collaborative multi-period fault recovery problem model of the power distribution network is considered to comprise an objective function and constraint conditions, wherein the constraint conditions comprise node power balance constraint neglecting network loss, injection power limit constraint, SOC constraint and topology constraint.
It should be understood that the grid loss factor a is used to represent the load level of the distribution grid, and its specific value may be set as desired. For example, in some embodiments, a constant of 0.1 may be set, and assuming that the total load capacity in the example is 3MW, the network loss is ignored and the load is calculated as 3 × 1.1 — 3.3 MW.
Further, in a preferred embodiment provided by the present invention, step S1 specifically includes the following steps:
the method comprises the steps of establishing an objective function maxf of a maximum weighted load power supply time (namely the load power supply time with the original capacity expanded by 1+ a times) required by a power distribution network in fault recovery1＝∑t∑i∈Lwiγi,t(1) (ii) a In the formula, wiIs the weight coefficient of the load; gamma rayi,tThe load i is in the state of t period, and the load is in the recovery state, gamma i,t1, otherwise 0;
adding a first constraint condition, and establishing a multi-period mixed integer linear model; the first constraint condition comprises a power flow constraint, an operation safety constraint, an energy and energy storage state of charge (SOC) constraint and a topological constraint;
the power flow constraint comprises the following steps:
Figure BDA0002613172150000061
Figure BDA0002613172150000062
Figure BDA0002613172150000063
Figure BDA0002613172150000064
Figure BDA0002613172150000065
Figure BDA0002613172150000071
Figure BDA0002613172150000072
equations (2) - (5) are node power balance constraints; equation (6) is in the form of a branch power matrix. In the formula, Vi,tIs a complex voltage of node I in time period t, Iij,tA complex current for line (i, j) over time period t; pki,tAnd Pij,tActive power variable, P, for lines (k, i) and (i, j) respectively during time period ti,tInjecting an active power variable for the node i in a time period t; qki,tAnd Qij,tReactive power variable, Q, for lines (k, i) and (i, j) respectively, during time ti,tInjecting a reactive power variable for node i at time t;
Figure BDA0002613172150000073
the active demand for the load connected to the inode during time period t,
Figure BDA0002613172150000074
the active power injected into the node for the time period t for the power supply connected to the inode,
Figure BDA0002613172150000075
the reactive demand for the load connected at the inode during time period t,
Figure BDA0002613172150000076
injecting reactive power into the node for a time period t for a power supply connected to the node i; sij,tIs the complex power variable of the line (i, j) over time period t;
operational safety constraints include:
Figure BDA0002613172150000077
Figure BDA0002613172150000078
Figure BDA0002613172150000079
equations (9) - (11) represent the capacity limit constraints for different types of power supplies. In the formula (I), the compound is shown in the specification,
Figure BDA00026131721500000710
and
Figure BDA00026131721500000711
respectively representing the maximum charging power and the maximum discharging power of an energy storage or electric vehicle charging pile connected to the node i;
energy and stored energy state of charge constraints include:
Figure BDA00026131721500000712
Figure BDA00026131721500000713
in the formula, Ei,0The energy value of the power generation resource internally remaining before restoration for the power source i. K is equal to [0, 1 ]]Is the state of charge of the battery-type device,
Figure BDA00026131721500000714
and
Figure BDA00026131721500000715
respectively satisfying the upper and lower limits of the state of charge, K, of the normal operation of the equipment ii,0For initial state of charge of the apparatus, piConverting energy to a state of charge of the device for the conversion factor;
the topological constraints include:
Figure BDA00026131721500000718
Figure BDA00026131721500000716
Figure BDA00026131721500000717
r is a set formed by all root nodes, and the final sum of the number of lines is equal to the number of the nodes minus the number of the root nodes; n is the set of all nodes contained in the target island, alphaijThe variable is 0-1, indicating whether lines (i, j) are connected, 1 is connected, and 0 is not connected. FijRepresenting virtual branch flow, DiFor dummy load, M is a larger positive real number, and alpha is a when the lines are not connectedij＝0，Fij＝0。
Further, step S2 specifically includes the following steps:
establishing a multi-period network loss and minimum objective function
minPloss＝Pgen-Pload (17)；
Adding a second constraint condition, and establishing a multi-period Optimal Power Flow (OPF) model; the second constraint includes:
node complex power balance constraints
Figure BDA0002613172150000081
Figure BDA0002613172150000082
Matrix form derived from ohm's law
Figure BDA0002613172150000083
Figure BDA0002613172150000084
Second order cone constraint
Figure BDA0002613172150000085
The process of solving the multi-period SOCP model further comprises the following steps:
the load is classified into n classes according to the importance of the load. The loads of the same level have the same weight coefficient. Defining the primary load as most important, i.e. w1>…>wn；
Adding a third constraint condition before a multi-period second-order cone programming (SOCP) model, wherein the third constraint condition comprises the following steps:
k for any two corresponding levels1And k2Load i and load j, the load amounts being Pload,iAnd Pload,jWhen k is1<k2Satisfy the following requirements
Figure BDA0002613172150000086
For a load of any level j,
Figure BDA0002613172150000088
for the load set with the importance level of k, | · | represents the number of elements of the corresponding set, and satisfies
Figure BDA0002613172150000087
The integer variables in the power distribution network fault recovery optimization model mainly comprise an integer variable gamma for representing whether the load is recoveredi,tSince the solution is performed in step S1, the second order cone model of the optimal power flow solution calculated in the known topological state is the output of the generator.
The invention also provides an embodiment, which exemplarily shows the application of the decoupling method provided by the invention.
In the test system shown in fig. 3, there are 123 nodes in total, the load is divided into three levels, the weight coefficient of the primary important load is 100, the weight coefficient of the secondary important load is 10, and the weight coefficient of the ordinary load is 0.2. After the test scene is an extreme event, the connection between the power distribution network represented by the test system and the large power grid is interrupted, the distributed power supply enters an independent operation state, and the load in the test system is completely cut off. The failure points within the system are located between the node 150 substation, the node 105 and 108, the nodes 63-64, and the nodes 78-79, respectively, and the failures have been isolated.
The method comprises the following steps: according to the information and the scene information of the test system shown in fig. 3, voltage constraints are ignored, the voltage of each node is approximated to be a rated voltage, after the network loss is converted into a load, an MILP model in multiple periods is established and solved, and the load state and the topological state are determined.
Step two: under the condition that the integer variable value is determined, a multi-period SOCP model is established, and the output of the generator and the power flow data in the model are solved.
The recovery results are shown in fig. 4, and are consistent with the results obtained by directly solving the original MISOCP model.
The solving time is shown in table 1, and it can be seen that the solving time is obviously shortened by applying the decoupling solving algorithm when the time interval is large.
TABLE 1 solving time comparison
Figure BDA0002613172150000091
In summary, the decoupling method for the multi-period fault recovery model of the power distribution network provided by the invention comprises two steps: neglecting voltage constraint, approximating each node voltage as rated voltage, determining a network loss factor a representing the network loss level according to the load level of the network in a normal operation state, calculating the load as (1+ a) times of the original capacity, establishing and solving a multi-period MILP model, and determining the load state and the topology state; under the condition that the integer variable value is determined, a multi-period SOCP model is established, and the output of the generator and the power flow data in the model are solved. The decoupling method provided by the invention can shorten the solving time of a Mixed Integer Second Order Cone Programming (MISOCP) model of a multi-period fault recovery problem, realize the rapid generation of a recovery strategy and simultaneously ensure the solving precision.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of software products, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (4)

1. A decoupling method for a multi-period fault recovery model of a power distribution network is characterized by comprising the following steps:
s1, approximating the voltage of each node in the power distribution network to a rated voltage, and determining a network loss factor a for representing the network loss level of the power distribution network; calculating the load level of the power distribution network to be 1+ a times of the original capacity, establishing and solving a multi-period mixed integer linear model, and obtaining the load state and the topological state of the power distribution network;
s2, based on the load state and the topological state of the power distribution network, a multi-period second-order cone planning model is established and solved, and a power distribution network recovery scheme is obtained.
2. The decoupling method of claim 1, wherein the step S1 specifically includes:
establishing an objective function maxf of a power distribution network for maximizing weighted load power supply time in fault recovery1＝∑t∑i∈ Lwiγi,t(1) (ii) a In the formula, wiIs the weight coefficient of the load; gamma rayi,tThe load i is in the state of t period, and the load is in the recovery state, gammai,t1, otherwise 0;
adding a first constraint condition, and establishing a multi-period mixed integer linear model; the first constraint condition comprises a power flow constraint, an operation safety constraint, an energy and energy storage charge state constraint and a topology constraint;
the power flow constraint includes:
Figure FDA0002613172140000011
Figure FDA0002613172140000012
Figure FDA0002613172140000013
Figure FDA0002613172140000014
Figure FDA0002613172140000015
Figure FDA0002613172140000016
Figure FDA0002613172140000017
in the formula, Vi,tIs a complex voltage of node I in time period t, Iij,tA complex current for line (i, j) over time period t; pki,tAnd Pij,tActive power variable, P, for lines (k, i) and (i, j) respectively during time period ti,tInjecting an active power variable for the node i in a time period t; qki,tAnd Qij,tReactive power variable, Q, for lines (k, i) and (i, j) respectively, during time ti,tInjecting a reactive power variable for node i at time t;
Figure FDA0002613172140000018
the active demand for the load connected to the inode during time period t,
Figure FDA0002613172140000019
the active power injected into the node for the time period t for the power supply connected to the inode,
Figure FDA00026131721400000110
the reactive demand for the load connected at the inode during time period t,
Figure FDA0002613172140000021
injecting reactive power into the node for a time period t for a power supply connected to the node i; sij,tIs the complex power variable of the line (i, j) over time period t;
the operational safety constraints include:
Figure FDA0002613172140000022
Figure FDA0002613172140000023
Figure FDA0002613172140000024
in the formula, Pi ch-maxAnd Pi dch-maxRespectively representing the maximum charging power and the maximum discharging power of an energy storage or electric vehicle charging pile connected to the node i;
the energy and storage state of charge constraints include:
Figure FDA0002613172140000025
Figure FDA0002613172140000026
in the formula, Ei,0The energy value of the power generation resource internally remaining before restoration for the power source i. K is equal to [0, 1 ]]Is the state of charge of the battery-type device,
Figure FDA0002613172140000027
and
Figure FDA0002613172140000028
are respectively provided withTo meet the upper and lower limits of the state of charge, K, for normal operation of the plant ii,0For initial state of charge of the apparatus, piConverting energy to a state of charge of the device for the conversion factor;
the topological constraints include:
Figure FDA0002613172140000029
Figure FDA00026131721400000210
Figure FDA00026131721400000211
r is a set formed by all root nodes, and the final sum of the number of lines is equal to the number of the nodes minus the number of the root nodes; n is the set of all nodes contained in the target island, alphaijThe variable is 0-1, indicating whether lines (i, j) are connected, 1 is connected, and 0 is not connected. FijRepresenting virtual branch flow, DiFor dummy load, M is a larger positive real number, and alpha is a when the lines are not connectedij＝0，Fij＝0。
3. The decoupling method of claim 2, wherein the step S2 specifically includes:
establishing a multi-period network loss and minimum objective function
minPloss＝Pgen-Pload (17)；
Adding a second constraint condition, and establishing a multi-period optimal power flow model; the second constraint includes:
node complex power balance constraints
Figure FDA0002613172140000031
Figure FDA0002613172140000032
Matrix form derived from ohm's law
Figure FDA0002613172140000033
Figure FDA0002613172140000034
Second order cone constraint
Figure FDA0002613172140000035
4. The decoupling method of claim 3, wherein solving the multi-period second order cone programming model to obtain the power distribution network recovery scheme further comprises:
sequencing each node of the power distribution network according to the load grade to obtain w1>…>wn；
Adding a third constraint condition before solving the second-order cone programming model of the multiple periods, wherein the third constraint condition comprises the following steps:
any two corresponding grades are respectively k1And k2Load i and load j, the load amounts being Pload,iAnd Pload,jWhen k is1<k2Satisfy the following requirements
Figure FDA0002613172140000036
The load of any level j is selected,
Figure FDA0002613172140000037
for a set of loads with an importance level of kAnd | represents the number of elements of the corresponding set, and satisfies
Figure FDA0002613172140000038
CN202010761329.5A 2020-07-31 2020-07-31 Decoupling method of multi-period fault recovery model of power distribution network Pending CN111952964A (en)

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CN112886573A (en) * 2021-01-25 2021-06-01 浙江大学 Power system recovery method and device considering operation performance
CN117277446A (en) * 2023-11-23 2023-12-22 浙江优能电力设计有限公司 Multi-target power distribution network planning method and system
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