CN114841548A - Power distribution network resource aggregation method, device, equipment and storage medium - Google Patents
Power distribution network resource aggregation method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN114841548A CN114841548A CN202210449225.XA CN202210449225A CN114841548A CN 114841548 A CN114841548 A CN 114841548A CN 202210449225 A CN202210449225 A CN 202210449225A CN 114841548 A CN114841548 A CN 114841548A
- Authority
- CN
- China
- Prior art keywords
- power
- node
- distribution network
- power distribution
- constraint
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000002776 aggregation Effects 0.000 title claims abstract description 25
- 238000004220 aggregation Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 title claims abstract description 22
- 230000009194 climbing Effects 0.000 claims abstract description 9
- 239000011159 matrix material Substances 0.000 claims description 50
- 230000035945 sensitivity Effects 0.000 claims description 38
- 230000008859 change Effects 0.000 claims description 35
- 230000004931 aggregating effect Effects 0.000 claims description 6
- 239000003795 chemical substances by application Substances 0.000 claims description 6
- 238000002347 injection Methods 0.000 claims description 6
- 239000007924 injection Substances 0.000 claims description 6
- 239000000243 solution Substances 0.000 claims description 6
- 235000003913 Coccoloba uvifera Nutrition 0.000 claims description 4
- 240000008976 Pterocarpus marsupium Species 0.000 claims description 4
- 239000013598 vector Substances 0.000 claims description 4
- 238000006165 Knowles reaction Methods 0.000 claims description 3
- 230000001788 irregular Effects 0.000 claims description 3
- 230000004044 response Effects 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 7
- 230000003287 optical effect Effects 0.000 description 6
- 230000002093 peripheral effect Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Power Engineering (AREA)
- Biodiversity & Conservation Biology (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a method, a device, equipment and a storage medium for power distribution network resource aggregation, which relate to the technical field of power consumer demand response modeling and comprise the following steps: step 1, collecting data information of a grid structure, node safety constraints, line safety constraints and the like of a power distribution network; step 2, collecting data information of power, climbing, energy constraint and the like of different flexible resources in the power distribution network; step 3, constructing a group of corresponding generators for each constraint based on the basic theory of the Knoop polyhedron to generate hyperplanes corresponding to the constraints in a solution space, and simplifying and deleting the generators corresponding to all the constraints; step 4, depicting a safe feasible domain after different flexible resources are aggregated based on the finally obtained generator set; meanwhile, when the method is used, a group of corresponding generators is constructed for each constraint in the aggregation problem by basing on the basic theory of the Knoop polyhedron, and the generators are used for generating the hyperplane of the corresponding constraint in the solution space.
Description
Technical Field
The invention relates to the technical field of power consumer demand response modeling, in particular to a power distribution network resource aggregation method, device, equipment and storage medium.
Background
The small controllable loads distributed on the demand side have the potential of supplementing the flexibility of the system, and the small controllable loads comprise temperature control loads, electric vehicles, distributed energy storage and the like, all of which can participate in the balance of the system through a certain control method, and are collectively called as demand side flexibility resources. However, it is difficult to perform quantitative analysis and cooperative control on a large number of demand-side flexibility resources, and in order to fully utilize the potential flexibility and reduce the calling difficulty and the calculation complexity, the demand-side flexibility resources must be aggregated and formed into a concise form for unified calling.
Disclosure of Invention
In order to solve the defects mentioned in the background art, the invention aims to provide a power distribution network resource aggregation method, a device, equipment and a storage medium.
The purpose of the invention can be realized by the following technical scheme:
a power distribution network resource aggregation method comprises the following steps:
and 4, depicting the safe feasible domain after the aggregation of different flexible resources based on the finally obtained generator set.
Further, the specific content in the step 1 is as follows:
(1) grid structure, node safety restraint and line safety restraint
The network structure information of the power distribution network comprises nodes and topology information of the power distribution network, and for the power distribution network with N nodes, the network structure information can be represented by an N × N adjacency matrix A, and the definition of elements aij in A is as follows:
the safety constraint of the nodes of the power distribution network refers to the voltage v of any node i in the power distribution network i The upper and lower limit constraints need to be satisfied, which is specifically expressed as:
v i ∈[v min ,v max ]
wherein v is min Represents the lower limit of the node voltage, v max Representing the upper node voltage limit.
The safety constraint of the power distribution network line refers to the branch power p of any branch k in the power distribution network i The upper and lower limit constraints need to be satisfied, which is specifically expressed as:
p i ∈[p min ,p max ]
wherein p is min Denotes the lower limit of the line power, p max Representing the upper line power limit.
(2) Voltage load sensitivity matrix
The voltage load sensitivity matrix comprises an active voltage load sensitivity matrix and a reactive voltage load sensitivity matrix, and represents the influence of active/reactive power injection at a certain node on the voltage of each node of the power distribution network. For active node load sensitivity matrix VLSM p In the description, the element p ij Which represents the change in voltage at node j after node i has injected 1 unit of active power. For reactive node load sensitivity matrix VLSM q In other words, the element q therein ij Which represents the change in voltage at node j after 1 unit of reactive power is injected at node i. The total voltage change of the nodes is caused by the active power and the reactive power of each node together, and is represented as follows:
|δV|＝|VLSM P ||δP|+|VLSM P ||δQ|
i.e.,
in the formula, δ V (n) represents the voltage change of the node nChemical conversion, δ P n Representing the active power change of node N, δ Q n Representing the reactive power change at node N.
(3) Tidal current load sensitivity matrix
The power flow load sensitivity matrix comprises an active power flow load sensitivity matrix and a reactive power flow load sensitivity matrix, and represents the influence of active/reactive power injection at a certain node on power flow of each line of the power distribution network. Assuming that the power distribution network has N nodes and M branches, a sensitivity matrix PLSM for active power flow load p In other words, the element p therein mn Which represents the change in active power on line m after node n has injected 1 unit of active power. Load sensitivity matrix PLSM for reactive nodes q In other words, the element q therein mn The change of the reactive power flow on the line m after injecting 1 unit of reactive power into the node n is represented as:
|δP|＝|PLSM P ||δP|
|δQ|＝|PLSM q ||δQ|
i.e.,
wherein, δ P (n) represents the active power flow change of the line n, δ Q (n) represents the reactive power flow change of the line n, and δ P n Representing the active power change of node n, δ Q n Representing the reactive power change at node n.
Further, the specific content in step 2 is as follows:
the power constraint of the flexible resource of the power distribution network indicates that the power output of the flexible resource needs to meet upper and lower limit constraints, the climbing constraint indicates that the power difference value of the flexible resource in two adjacent time intervals needs to meet the upper and lower limit constraints, and the energy constraint indicates that the total output of the flexible resource in a certain continuous time period (including a plurality of time intervals) needs to meet the upper and lower limit constraints.
Further, the specific content in step 3 includes:
for constraints of the form i Denotes the ith variable, λ i And c is a constant.
The following group of generators is constructed to ensure that the finally generated knowless polyhedron has a hyperplane parallel to the constraint, and the specific form is as follows:
in the formula, g i Is the ith generator. After a group of generators is constructed based on each constraint, the generators in the same direction or the opposite direction are deleted, which is specifically expressed as: if g is i ＝g j Then g is i And g j In the same direction as g i ＝-g j Then g is i And g j And the opposite direction.
Further, the specific content in the step 4 is as follows:
for the problem of obtaining the maximum inscribed Cherokee polyhedron Z in the original irregular feasible region P, the feasible region P meets the constraint
Ax≤b
Wherein A, b is a coefficient matrix and x is a variable matrix. Arbitrarily constructing S normal vectors
By solving a linear programming problem for finding at α s The diameter of the directionally feasible region Z, P defines similarity in terms of its position versus length relationship:
in the formula:
respectively two feasible domains are in alpha s Diameter in the direction. Lambda s ∈[0,1]Closer to 1 represents higher similarity.
Known as alpha s The linear programming problem of finding convex polyhedron tangents and calculating diameters can be written as:
s.t.Ax≤b
where ε is a sufficiently large constant.
Kino polyhedron point of tangency and diameter and alpha s The relationship of (c) can be written as:
wherein G is a generator group, β max Is the maximum web length of the generator.
Obtained by solving 2S linear programming problems
Later, the knoeveness polyhedron approximation problem can be written as:
s.t.Ac+|AG|β max ≤b
wherein c is the central point of the knoeveness polyhedron, Ac + | AG | beta max B is less than or equal to b can ensure
The invention has the beneficial effects that:
based on the basic theory of the Knoop polyhedron, aiming at each constraint in the aggregation problem, constructing a group of corresponding generators for generating hyperplanes corresponding to the constraints in a solution space, and simplifying and deleting the generators corresponding to all the constraints; and describing a safe feasible domain after different flexible resources are aggregated based on the finally obtained generator set, and finally realizing effective aggregation of massive distributed resources.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of a 5-node power distribution network;
FIG. 3 is a schematic diagram of a flexible resource security aggregation determination apparatus;
fig. 4 is a schematic structural diagram of a flexible resource security aggregation device.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "opening," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like are used in an orientation or positional relationship that is merely for convenience in describing and simplifying the description, and do not indicate or imply that the referenced component or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be considered as limiting the present invention.
A power distribution network resource aggregation method, device, equipment and storage medium are disclosed, as shown in the figure, the method comprises the following steps:
and 4, depicting the safe feasible domain after the aggregation of different flexible resources based on the finally obtained generator set.
The concrete contents in the step 1 are as follows:
(1) grid structure, node safety restraint and line safety restraint
The network structure information of the power distribution network comprises nodes and topology information of the power distribution network, and for the power distribution network with N nodes, the network structure information can be represented by an N × N adjacency matrix A, and the definition of elements aij in A is as follows:
the safety constraint of the nodes of the power distribution network refers to the voltage v of any node i in the power distribution network i The upper and lower limit constraints need to be satisfied, which is specifically expressed as:
v i ∈[v min ,v max ]
wherein v is min Represents the lower limit of the node voltage, v max Representing the upper node voltage limit.
The safety constraint of the power distribution network line refers to the branch power p of any branch k in the power distribution network i The upper and lower limit constraints need to be satisfied, which is specifically expressed as:
p i ∈[p min ,p max ]
wherein p is min Denotes the lower line power limit, p max Representing the upper line power limit.
(2) Voltage load sensitivity matrix
The voltage load sensitivity matrix comprises an active voltage load sensitivity matrix and a reactive voltage load sensitivity matrix, and represents the influence of active/reactive power injection at a certain node on the voltage of each node of the power distribution network. For the active powerNode load sensitivity matrix VLSM p In other words, the element p therein ij Which represents the change in voltage at node j after node i has injected 1 unit of active power. For reactive node load sensitivity matrix VLSM q In other words, the element q therein ij Representing the change in voltage at node j after 1 unit of reactive power is injected at node i. The total voltage change of the nodes is caused by the active power and the reactive power of each node together, and is represented as follows:
|δV|＝|VLSM P ||δP|+|VLSM P ||δQ|
i.e.,
wherein δ V (n) represents a voltage change at the node n, δ P n Representing the active power change of node N, δ Q n Representing the reactive power change at node N.
(3) Tidal current load sensitivity matrix
The power flow load sensitivity matrix comprises an active power flow load sensitivity matrix and a reactive power flow load sensitivity matrix, and represents the influence of active/reactive power injection at a certain node on power flow of each line of the power distribution network. Assuming that the power distribution network has N nodes and M branches, a sensitivity matrix PLSM for the active power flow load p In other words, the element p therein mn Which represents the change in active power on line m after 1 unit of active power is injected at node n. Load sensitivity matrix PLSM for reactive nodes q In other words, the element q therein mn The change of the reactive power flow on the line m after injecting 1 unit of reactive power into the node n is represented as:
|δP|＝|PLSM P ||δP|
|δQ|＝|PLSM q ||δQ|
i.e.,
wherein, δ P (n) represents the active power flow change of the line n, δ Q (n) represents the reactive power flow change of the line n, and δ P n Representing the active power change of node n, δ Q n Representing the reactive power change at node n.
The specific contents in the step 2 are as follows:
the power constraint of the flexible resource of the power distribution network indicates that the power output of the flexible resource needs to meet upper and lower limit constraints, the climbing constraint indicates that the power difference value of the flexible resource in two adjacent time intervals needs to meet the upper and lower limit constraints, and the energy constraint indicates that the total output of the flexible resource in a certain continuous time period (including a plurality of time intervals) needs to meet the upper and lower limit constraints.
4. The method for aggregating power distribution network resources according to claim 1, wherein the specific content in step 3 is as follows:
for constraints of the form i Denotes the ith variable, λ i Represents the parameter corresponding to the ith variable, and c is a constant.
The following group of generators is constructed to ensure that the finally generated knowless polyhedron has a hyperplane parallel to the constraint, and the specific form is as follows:
in the formula, g i Is the ith generator. After a group of generators is constructed based on each constraint, the generators in the same direction or the opposite direction are deleted, which is specifically expressed as: if g is i ＝g j Then g is i And g j In the same direction as g i ＝-g j Then g is i And g j And the reverse direction.
The concrete contents in the step 4 are as follows:
for the problem of obtaining the maximum inscribed Cherokee polyhedron Z in the original irregular feasible region P, the feasible region P meets the constraint
Ax≤b
Wherein A, b is a coefficient matrix and x is a variable matrix. Arbitrarily constructing S normal vectors
By solving a linear programming problem for finding at α s The diameter of the directionally feasible region Z, P defines similarity in terms of its position versus length relationship:
in the formula:
respectively two feasible domains are in alpha s Diameter in the direction. Lambda s ∈[0,1]Closer to 1 represents higher similarity.
Known as alpha s The linear programming problem of finding convex polyhedron tangents and calculating diameters can be written as:
s.t.Ax≤b
where ε is a sufficiently large constant.
Kino polyhedron point of tangency and diameter and alpha s The relationship of (c) can be written as:
wherein G is a generator group, β max The maximum width of the generator.
Obtained by solving 2S linear programming problems
Later, the knoeveness polyhedron approximation problem can be written as:
s.t.Ac+|AG|β max ≤b
wherein c is the central point of the knoeveness polyhedron, Ac + | AG | beta max B is less than or equal to b can ensure
Example 1
Fig. 2 is a diagram of a 5-node topology disassembled from a 14-node network according to IEEE standard, on which an example analysis is performed.
Assuming that the demand response event lasts 2 hours, the DR response power of the aggregators at the nodes 3 and 4 is respectively
And
the fixed load remains constant for three periods.
1. Constraining
(1) Power constraint
(2) Energy confinement
(3) Climbing restraint
(4) Line flow constraint
Wherein the lines 1-2 are prone to tidal current violations.
Simplifying to obtain:
(5) node voltage constraint
Where node 4 is susceptible to voltage violations.
Simplifying to obtain:
and (3) integrating all the constraints, and constructing the A matrix, the b matrix and the G matrix as follows:
taking a normal vector:
the central coordinates of the knowlett-packard polyhedron are obtained as follows: [0.875,1.125,1.125,0.875], the scaling factor β of each generator was: [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.88388348,0.35355339,0.88388348,0.35355339,0.35355339,0.0,0.0].
Example 2
Fig. 3 is a schematic diagram of a flexible resource security aggregation determining apparatus according to embodiment 2 of the present invention. The embodiment can be applied to the case of performing secure aggregation on the target flexible resource, and the apparatus can be implemented in a software and/or hardware manner, and the apparatus can be configured in a terminal device. The flexible resource security aggregation determining apparatus includes: a distribution network parameter acquisition module 410 and a flexible resource parameter acquisition module 420.
The power distribution network parameter acquisition module 410 is configured to acquire data information of a grid structure, node safety constraints, line safety constraints, and the like of a power distribution network; the flexible resource parameter acquiring module 420 is configured to acquire data information of power, climbing, energy constraint, and the like of different flexible resources in the power distribution network.
According to the technical scheme of the embodiment, based on the basic theory of the Kino polyhedron, a group of corresponding generators are constructed for each constraint and used for generating the hyperplane of the corresponding constraint in a solution space, and the generators corresponding to all the constraints are simplified and subtracted; and depicting the safe feasible domain after the aggregation of different flexible resources based on the finally obtained generator set. The method can accurately model the demand response adjustment capability of the large-quantity flexible resources in the range of the power distribution network for power dispatching personnel, and supports the distributed resources to participate in the safe and economic adjustment of the power grid.
Example 3
Fig. 4 is a schematic structural diagram of an apparatus provided in embodiment 3 of the present invention, where the embodiment of the present invention provides a service for implementing flexible resource security aggregation in the above-mentioned embodiment of the present invention, and a flexible resource security aggregation determining device in the above-mentioned embodiment may be configured. Fig. 3 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 3 is only an example and should not impose any limitation on the functionality and scope of use of embodiments of the present invention.
As shown in FIG. 3, device 12 is in the form of a general purpose computing device. The components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement the demand response capability quantifying method provided by the embodiment of the present invention.
Through the equipment, the problem of safe aggregation of the large-quantity flexible resources in the power distribution network is solved.
Example 4
a power distribution network parameter acquisition module is acquired;
and inputting the measured parameters into a flexible resource security aggregation model which is trained in advance to obtain an aggregation feasible domain of the current resources under the security constraint of the power distribution network.
Computer storage media for embodiments of the present invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (5)
1. A power distribution network resource aggregation method is characterized by comprising the following steps:
step 1, collecting data information of a grid structure, node safety constraints, line safety constraints and the like of a power distribution network;
step 2, collecting data information of power, climbing, energy constraint and the like of different flexible resources in the power distribution network;
step 3, constructing a group of corresponding generators for each constraint based on the basic theory of the Knoop polyhedron to generate hyperplanes corresponding to the constraints in a solution space, and simplifying and deleting the generators corresponding to all the constraints;
and 4, depicting the safe feasible domain after the aggregation of different flexible resources based on the finally obtained generator set.
2. The method for aggregating resources of a power distribution network according to claim 1, wherein the specific content in the step 1 is as follows:
(1) grid structure, node safety restraint and line safety restraint
The network structure information of the power distribution network comprises nodes and topology information of the power distribution network, and for the power distribution network with N nodes, the network structure information can be represented by an N × N adjacency matrix A, and the definition of elements aij in A is as follows:
the safety constraint of the nodes of the power distribution network refers to the voltage v of any node i in the power distribution network i The upper and lower limit constraints need to be satisfied, which is specifically expressed as:
v i ∈[v min ,v max ]
wherein v is min Represents the lower limit of the node voltage, v max Representing the upper node voltage limit.
The safety constraint of the power distribution network line refers to the branch power p of any branch k in the power distribution network i The upper and lower limit constraints need to be satisfied, which is specifically expressed as:
p i ∈[p min ,p max ]
wherein p is min Denotes the lower line power limit, p max Representing the upper line power limit.
(2) Voltage load sensitivity matrix
The voltage load sensitivity matrix comprises an active voltage load sensitivity matrix and a reactive voltage load sensitivity matrix, and represents the influence of active/reactive power injection at a certain node on the voltage of each node of the power distribution network. For active node load sensitivity matrix VLSM p In other words, the element p therein ij Which represents the change in voltage at node j after node i has injected 1 unit of active power. For reactive node load sensitivity matrix VLSM q In other words, the element q therein ij Representing the change in voltage at node j after 1 unit of reactive power is injected at node i. The total voltage change of the nodes is caused by the active power and the reactive power of each node together, and is represented as follows:
|δV|＝|VLSM P ||δP|+|VLSM P ||δQ| (1)
i.e.,
wherein δ V (n) represents a voltage change at the node n, δ P n Representing active power variation of node N, δ Q n Representing the reactive power change at node N.
(3) Tidal current load sensitivity matrix
The tidal current load sensitivity matrix comprises an active tidal current load sensitivity matrix and a reactive tidal current load sensitivity matrix, and represents that the active tidal current load sensitivity matrix and the reactive tidal current load sensitivity matrix are arranged in a certain sectionThe active/reactive power injection of points has influence on the power flow of each line of the power distribution network. Assuming that the power distribution network has N nodes and M branches, a sensitivity matrix PLSM for the active power flow load p In other words, the element p therein mn Which represents the change in active power on line m after node n has injected 1 unit of active power. Load sensitivity matrix PLSM for reactive nodes q In other words, the element q therein mn The change of the reactive power flow on the line m after injecting 1 unit of reactive power into the node n is represented as:
|δP|＝|PLSM P ||δP|
|δQ|＝|PLSM q ||δQ|
i.e.,
wherein, δ P (n) represents the active power flow change of the line n, δ Q (n) represents the reactive power flow change of the line n, and δ P n Representing the active power change of node n, δ Q n Representing the reactive power change at node n.
3. The method for aggregating resources of a power distribution network according to claim 1, wherein the specific content in step 2 is as follows:
the power constraint of the flexible resource of the power distribution network indicates that the power output of the flexible resource needs to meet upper and lower limit constraints, the climbing constraint indicates that the power difference value of the flexible resource in two adjacent time intervals needs to meet the upper and lower limit constraints, and the energy constraint indicates that the total output of the flexible resource in a certain continuous time period (including a plurality of time intervals) needs to meet the upper and lower limit constraints.
4. The method for aggregating power distribution network resources according to claim 1, wherein the specific content in step 3 is as follows:
for constraints of the form i Denotes the ith variable, λ i And c is a constant.
The following group of generators is constructed to ensure that the finally generated knowless polyhedron has a hyperplane parallel to the constraint, and the specific form is as follows:
in the formula, g i Is the ith generator. After a group of generators is constructed based on each constraint, the generators in the same direction or the opposite direction are deleted, which is specifically expressed as: if g is i ＝g j Then g is i And g j In the same direction as g i ＝-g j Then g is i And g j And the reverse direction.
5. The method for aggregating power distribution network resources according to claim 1, wherein the specific content in step 4 is as follows:
for the problem of obtaining the maximum inscribed Cherokee polyhedron Z in the original irregular feasible region P, the feasible region P meets the constraint
Ax≤b
Wherein A, b is a coefficient matrix and x is a variable matrix. Arbitrarily constructing S normal vectors
By solving a linear programming problem for finding at α s The diameter of the directionally feasible region Z, P defines similarity in terms of its position versus length relationship:
in the formula:
respectively two feasible domains are in alpha s Diameter in the direction. Lambda s ∈[0,1]Closer to 1 represents higher similarity.
Known as alpha s The linear programming problem of finding convex polyhedron tangents and calculating diameters can be written as:
s.t.Ax≤b
where ε is a sufficiently large constant.
Kino polyhedron point of tangency and diameter and alpha s The relationship of (c) can be written as:
wherein G is a generator group, β max Is the maximum web length of the generator.
Obtained by solving 2S linear programming problems
Later, the knoeveness polyhedron approximation problem can be written as:
s.t.Ac+|AG|β max ≤b
wherein c is the central point of the knoeveness polyhedron, Ac + | AG | beta max B is less than or equal to b can ensure
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210449225.XA CN114841548A (en) | 2022-04-26 | 2022-04-26 | Power distribution network resource aggregation method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210449225.XA CN114841548A (en) | 2022-04-26 | 2022-04-26 | Power distribution network resource aggregation method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114841548A true CN114841548A (en) | 2022-08-02 |
Family
ID=82566982
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210449225.XA Pending CN114841548A (en) | 2022-04-26 | 2022-04-26 | Power distribution network resource aggregation method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114841548A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118154260A (en) * | 2024-05-11 | 2024-06-07 | 国网浙江省电力有限公司丽水供电公司 | Power distribution network resource aggregation method, system, computer equipment and storage medium |
-
2022
- 2022-04-26 CN CN202210449225.XA patent/CN114841548A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118154260A (en) * | 2024-05-11 | 2024-06-07 | 国网浙江省电力有限公司丽水供电公司 | Power distribution network resource aggregation method, system, computer equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Akbari et al. | Linear approximated formulation of AC optimal power flow using binary discretisation | |
| Chen et al. | Steady‐state security assessment method based on distance to security region boundaries | |
| Guigues et al. | The value of rolling-horizon policies for risk-averse hydro-thermal planning | |
| US20140025351A1 (en) | Planning economic energy dispatch in electrical grid under uncertainty | |
| Marino et al. | A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics | |
| Shi et al. | Multistage robust energy management for microgrids considering uncertainty | |
| Vatankhah Barenji et al. | Optimally sized design of a wind/photovoltaic/fuel cell off-grid hybrid energy system by modified-gray wolf optimization algorithm | |
| Liu et al. | Interval optimal scheduling of hydro‐PV‐wind hybrid system considering firm generation coordination | |
| Zhang et al. | Probabilistic power flow calculation using the Johnson system and Sobol's quasi‐random numbers | |
| CN109449923B (en) | Quantitative analysis method for operation flexibility of active power distribution system and related product | |
| Chen et al. | Combined probabilistic forecasting method for photovoltaic power using an improved Markov chain | |
| Chen et al. | N− 1 security assessment approach based on the steady‐state security distance | |
| Shayesteh et al. | Scenario reduction, network aggregation, and DC linearisation: which simplifications matter most in operations and planning optimisation? | |
| CN115600717A (en) | Federal learning-based industry-divided power load prediction method, equipment and storage medium | |
| Lin et al. | Stochastic economic dispatch of power system with multiple wind farms and pumped‐storage hydro stations using approximate dynamic programming | |
| CN114841548A (en) | Power distribution network resource aggregation method, device, equipment and storage medium | |
| Lopez et al. | Quickflex: a fast algorithm for flexible region construction for the tso-dso coordination | |
| Ruiz et al. | Outer‐approximation method for security constrained unit commitment | |
| Dui et al. | Optimal unit commitment based on second‐order cone programming in high wind power penetration scenarios | |
| Rueda et al. | Probabilistic framework for risk analysis of power system small-signal stability | |
| CN116862172A (en) | Full-period variable time period ordered power utilization scheduling method and storage medium | |
| CN117791618A (en) | Power scheduling method, device, computer equipment and storage medium | |
| Kulikov et al. | Organization of control of the generalized power quality parameter using Wald’s sequential analysis procedure | |
| CN115481767A (en) | Operation data processing method and device for power distribution network maintenance and computer equipment | |
| Ramesh et al. | Electrical power system state estimation meter placement—a comparative survey report |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |