CN114219125A - High-elasticity urban power grid multi-dimensional intelligent partitioning method - Google Patents
High-elasticity urban power grid multi-dimensional intelligent partitioning method Download PDFInfo
- Publication number
- CN114219125A CN114219125A CN202111341637.3A CN202111341637A CN114219125A CN 114219125 A CN114219125 A CN 114219125A CN 202111341637 A CN202111341637 A CN 202111341637A CN 114219125 A CN114219125 A CN 114219125A
- Authority
- CN
- China
- Prior art keywords
- partition
- particle
- scheme
- power grid
- power
- 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
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000000638 solvent extraction Methods 0.000 title claims abstract description 22
- 238000005192 partition Methods 0.000 claims abstract description 68
- 239000002245 particle Substances 0.000 claims abstract description 40
- 238000011084 recovery Methods 0.000 claims abstract description 38
- 230000006870 function Effects 0.000 claims abstract description 20
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 11
- 238000004458 analytical method Methods 0.000 claims abstract description 4
- 230000003044 adaptive effect Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000010206 sensitivity analysis Methods 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 abstract description 3
- 230000033001 locomotion Effects 0.000 description 6
- 238000004088 simulation Methods 0.000 description 5
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 241001672018 Cercomela melanura Species 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 239000004020 conductor Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- Data Mining & Analysis (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (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 high-elasticity multi-dimensional intelligent partitioning method for an urban power grid, which comprises the following steps of: establishing an optimal partition strategy objective function according to the multi-dimensional data, and setting time constraint and power flow constraint; solving by adopting a particle swarm algorithm, and randomly initializing the value and the speed of each particle by setting the number N of the particles, the maximum iteration times, the maximum value and the minimum value of the inertia coefficient and a learning factor in a particle iteration formula to obtain a partitioning scheme; and taking the partition schemes which are not independent of each other as alternative schemes, taking the objective function value as an attribute, and finally adopting a grey multi-attribute decision method to perform decision analysis, wherein the scheme with the highest score is the optimal partition scheme. The influence of various dimensional factors on the partition is considered, and the target power grid is reasonably and dynamically topologically partitioned on the basis of different fault processing scenes; the necessary autonomous ability of each partition under the abnormal condition is ensured; during the fault recovery process, coordination support capability between different voltage levels and different areas is provided.
Description
Technical Field
The invention relates to the technical field of power grid partitioning, in particular to a high-elasticity urban power grid multi-dimensional intelligent partitioning method.
Background
The recovery of the power system after a major power failure is a complex decision and control problem, and in order to improve the recovery speed of the power grid, a large-scale power grid is generally divided into a plurality of partitions according to the structural characteristics of the power grid, each partition is recovered independently, and then the recovery of the whole system is realized by grid connection. In each subarea, the main unit, the hub node and the important load node are recovered to form an initial recovery grid frame, and then the initial recovery grid frame is radiated and expanded to the periphery gradually on the basis. However, many factors are involved in partitioning, and therefore, how to reasonably partition the power grid is an important problem in the recovery process after a major power failure.
Disclosure of Invention
Aiming at the problem that the power grid can not be reasonably partitioned in the prior art, the invention provides a high-elasticity urban power grid multi-dimensional intelligent partitioning method, which partitions the whole network according to the structural characteristics of the power grid, the distribution condition of a self-starting power supply and other factors by means of a target function and a particle swarm algorithm, so that the recovery speed of the partitioned power grid is optimal.
The technical scheme of the invention is as follows.
A high-elasticity urban power grid multi-dimensional intelligent partitioning method comprises the following steps:
establishing an optimal partition strategy objective function according to the multi-dimensional data, and setting time constraint and power flow constraint;
solving by adopting a particle swarm algorithm, and randomly initializing the value and the speed of each particle by setting the number N of the particles, the maximum iteration times, the maximum value and the minimum value of the inertia coefficient and a learning factor in a particle iteration formula to obtain a partitioning scheme;
and taking the partition schemes which are not independent of each other as alternative schemes, taking the objective function value as an attribute, and finally adopting a grey multi-attribute decision method to perform decision analysis, wherein the scheme with the highest score is the optimal partition scheme.
The basic idea of the invention is as follows: the self-starting power supply, part of non-self-starting power supply and some particularly important loads form an area through the backbone network, and then are cracked according to the above constraint. The principle of partitioning is to make the important load recovery time as short as possible. On one hand, for the unit without self-starting capability, the unit is connected to the self-starting unit at the minimum charging reactive power cost in the initial self-starting stage through reasonable partitioning; on the other hand, the closing operation times from the power supply point to the load point are introduced to express the load recovery speed. Considering that for the important load, the closer the important load is to the power supply point, the more beneficial the recovery is, therefore, the product of the load weight and the shortest distance from the load to the power supply point is introduced to measure the recovery speed of the important load.
Preferably, the optimal partition strategy objective function is as follows:
in the formula: p is the number of the designated system partitions and is determined by the number of the units with the self-starting capability; na (p) denotes a set of systems formed of a number of partitions with p; NA (p)kRepresents the k-th system formed by p partitions; NLiRepresents the set of overhead lines introduced in the ith zone in order to recover all the generators; NGiRepresenting a recovery unit set in the ith zone; NDiRepresenting a set of loads within the ith zone;
representing charging reactive power of a jth overhead line in an ith partition;
recovering the capacity of the unit for the g station in the ith partition;
the weight of the mth load in the ith partition;
the total number of lines required to be input from the mth load in the ith partition to the nearest generator set in the partition; the coefficient c represents the degree of importance to these two objectives.
Preferably, the time constraint includes:
for s ═ 1,2, ·, k partitions:
0＜Trj＜Tmcj,j＝1,2,...,Nsg
in the formula: t isrjObtaining a time interval from zero time to power supply starting for the unit j; t ismcjThe unit starting time limit of the allowance is considered; n is a radical ofsgThe number of crew nodes included in the partition s.
Preferably, the power flow constraint includes:
for s ═ 1,2, ·, k partitions:
Pi≤Pimax,i＝1,2,...,Ls
in the formula: n is a radical ofsnRecovering the number of nodes contained in the net rack for the partition; u shapeiIs the node voltage; piThe active power flowing through the branch i; pimaxThe maximum allowed power for branch i.
Preferably, for the scheme that the generated power flow exceeds the limit, the output and the load level of the generator are adjusted by adopting a sensitivity analysis method, and if the adjustment quantity is within an allowable range, the scheme is still considered to be feasible; otherwise, abandoning the scheme of out-of-limit trend.
Preferably, the solving by using a particle swarm algorithm includes:
initializing a population: randomly generating an initial population according to a designed coding mode, wherein each particle represents a group of partition schemes, and calculating an adaptive value;
constructing an external file: adding non-inferior particles into an external file based on a Pareto domination relation and by combining the adaptive value of each particle; update speed and position: the updating of the particle positions adopts a binary updating formula, in the speed updating, the required individual optimal positions are taken from the historical non-inferior individual positions of the particles, and the overall optimal positions are taken from the particle positions of the sparse distribution area in the external file by combining the self-adaptive grid method and the roulette method;
and (3) iteration termination conditions: and when the iteration times reach the preset maximum iteration times, stopping the calculation and outputting a result, otherwise, returning to the previous step for recalculation until the iteration termination condition is met.
The invention considers the principle that the partition scale and the recovery time are equivalent, and takes the minimum recovery time difference of each partition as a first objective function of an optimization model. Secondly, in the objective function, the safety and rapidity requirements of system partitions are comprehensively considered, on one hand, the charging reactive power of a line from a black start power supply to a recovery node is minimized, and the overvoltage problem of the line during power transmission is prevented; on the other hand, the number of times of switching operation in a recovery line and the number of power stations passing through the recovery line are minimized to accelerate the recovery speed of the nodes, and the model is solved by adopting a particle swarm algorithm.
The substantial effects of the invention include: the influence of various dimensionality factors on the subareas is considered, and the target power grid is reasonably and dynamically topologically partitioned on the basis of different fault processing scenes, so that a foundation is provided for fault self-healing recovery based on the subareas; the necessary autonomous capacity of each partition is ensured under abnormal conditions (such as large-area power failure caused by external power loss); during the fault recovery process, coordination support capability between different voltage levels and different areas is provided.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions will be clearly and completely described below with reference to the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all 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.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The technical solution of the present invention will be described in detail below with specific examples. Embodiments may be combined with each other and some details of the same or similar concepts or processes may not be repeated in some embodiments.
Example (b):
a high-elasticity urban power grid multidimensional intelligent partitioning method comprises the following steps as shown in figure 1:
establishing an optimal partition strategy objective function according to the multi-dimensional data, and setting time constraint and power flow constraint;
solving by adopting a particle swarm algorithm, and randomly initializing the value and the speed of each particle by setting the number N of the particles, the maximum iteration times, the maximum value and the minimum value of the inertia coefficient and a learning factor in a particle iteration formula to obtain a partitioning scheme;
and taking the partition schemes which are not independent of each other as alternative schemes, taking the objective function value as an attribute, and finally adopting a grey multi-attribute decision method to perform decision analysis, wherein the scheme with the highest score is the optimal partition scheme.
Typhoon uncertainty is not considered in most typhoon motion simulation researches at present, and typhoon motion path simulation and typhoon field simulation are not effectively combined. In the embodiment, uncertainty of typhoon landing parameters is considered, a typhoon occurrence scene is generated based on a Monte Carlo method, a forward selection algorithm is applied to extract a typical typhoon occurrence scene, and the typhoon occurrence scene can reflect typhoon occurrence characteristics of a typhoon frequent occurrence area. And for each typhoon scene, combining corresponding typhoon landing parameters, adopting a storm track model to simulate a typhoon motion path, and adopting a Batts wind field model to simulate a typhoon wind field.
And simulating a typhoon wind field by adopting a Batts wind field model, and simulating a movement path of the typhoon from the landing time to the death time. The main ideas of the typhoon motion path simulation are as follows: the position, the moving speed and the moving direction angle of the typhoon center at the landing time are used as input data, namely typhoon landing parameters are used as input data, based on a storm track model, a motion simulation result in typhoon at each time after landing and based on a typical typhoon scene can be calculated, and the load effect of the overhead conductor and the concrete pole under the typhoon weather is analyzed; and according to the structure reliability principle, by combining the probability distribution of the mechanical strength of the distribution line, a reliability model of the load of the overhead distribution line in typhoon weather is established, and a foundation is laid for the post-disaster partition model.
The basic idea of the embodiment is as follows: the self-starting power supply, part of non-self-starting power supply and some particularly important loads form an area through the backbone network, and then are cracked according to the above constraint. The principle of partitioning is to make the important load recovery time as short as possible. On one hand, for the unit without self-starting capability, the unit is connected to the self-starting unit at the minimum charging reactive power cost in the initial self-starting stage through reasonable partitioning; on the other hand, the closing operation times from the power supply point to the load point are introduced to express the load recovery speed. Considering that for the important load, the closer the important load is to the power supply point, the more beneficial the recovery is, therefore, the product of the load weight and the shortest distance from the load to the power supply point is introduced to measure the recovery speed of the important load.
After a power system has a major power failure, a dispatching department needs to adopt an active and effective recovery scheme to realize system recovery. For a large-scale network, the power grid is divided into several sub-networks to be independently recovered in parallel according to the structural characteristics of the power grid, and then the recovery of the whole system is realized by grid connection.
The optimal partition strategy objective function in this embodiment is:
in the formula: p is the number of the designated system partitions and is determined by the number of the units with the self-starting capability; na (p) denotes a set of systems formed of a number of partitions with p; NA (p)kRepresents the k-th system formed by p partitions; NLiRepresents the set of overhead lines introduced in the ith zone in order to recover all the generators; NGiRepresenting a recovery unit set in the ith zone; NDiRepresenting a set of loads within the ith zone;
representing charging reactive power of a jth overhead line in an ith partition;
recovering the capacity of the unit for the g station in the ith partition;
the weight of the mth load in the ith partition;
the total number of lines required to be input from the mth load in the ith partition to the nearest generator set in the partition; the coefficient c represents the degree of importance to these two objectives.
The time constraint in this embodiment includes:
for s ═ 1,2, ·, k partitions:
0＜Trj＜Tmcj,j＝1,2,...,Nsg
in the formula: t isrjObtaining a time interval from zero time to power supply starting for the unit j; t ismcjThe unit starting time limit of the allowance is considered; n is a radical ofsgThe number of crew nodes included in the partition s.
The power flow constraint in the embodiment includes:
for s ═ 1,2, ·, k partitions:
Pi≤Pimax,i＝1,2,...,Ls
in the formula：NsnRecovering the number of nodes contained in the net rack for the partition; u shapeiIs the node voltage; piThe active power flowing through the branch i; pimaxThe maximum allowed power for branch i.
In the embodiment, for the scheme that the generated power flow exceeds the limit, the output and the load level of the generator are adjusted by adopting a sensitivity analysis method, and if the adjustment amount is within an allowable range, the scheme is still considered to be feasible; otherwise, abandoning the scheme of out-of-limit trend.
In this embodiment, the solving using the particle swarm algorithm includes:
initializing a population: randomly generating an initial population according to a designed coding mode, wherein each particle represents a group of partition schemes, and calculating an adaptive value;
constructing an external file: adding non-inferior particles into an external file based on a Pareto domination relation and by combining the adaptive value of each particle; update speed and position: the updating of the particle positions adopts a binary updating formula, in the speed updating, the required individual optimal positions are taken from the historical non-inferior individual positions of the particles, and the overall optimal positions are taken from the particle positions of the sparse distribution area in the external file by combining the self-adaptive grid method and the roulette method;
and (3) iteration termination conditions: and when the iteration times reach the preset maximum iteration times, stopping the calculation and outputting a result, otherwise, returning to the previous step for recalculation until the iteration termination condition is met.
In this embodiment, the principle that the partition size and the recovery time are equivalent is considered, and the minimum recovery time difference of each partition is used as the first objective function of the optimization model. Secondly, in the objective function, the safety and rapidity requirements of system partitions are comprehensively considered, on one hand, the charging reactive power of a line from a black start power supply to a recovery node is minimized, and the overvoltage problem of the line during power transmission is prevented; on the other hand, the number of times of switching operation in a recovery line and the number of power stations passing through the recovery line are minimized to accelerate the recovery speed of the nodes, and the model is solved by adopting a particle swarm algorithm.
The substantial effects of the present embodiment include: the influence of various dimensionality factors on the subareas is considered, and the target power grid is reasonably and dynamically topologically partitioned on the basis of different fault processing scenes, so that a foundation is provided for fault self-healing recovery based on the subareas; the necessary autonomous capacity of each partition is ensured under abnormal conditions (such as large-area power failure caused by external power loss); during the fault recovery process, coordination support capability between different voltage levels and different areas is provided.
Through the description of the above embodiments, those skilled in the art will understand that, for convenience and simplicity of description, only the division of the above functional modules is used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of a specific device is divided into different functional modules to complete all or part of the above described functions.
In the embodiments provided in this application, it should be understood that the disclosed structures and methods may be implemented in other ways. For example, the above-described embodiments with respect to structures are merely illustrative, and for example, a module or a unit may be divided into only one logic function, and may have another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another structure, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, structures or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed to a plurality of different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially contributed to by the prior art, or all or part of the technical solutions may be embodied in the form of a software product, where the software product is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. A high-elasticity urban power grid multi-dimensional intelligent partitioning method is characterized by comprising the following steps:
establishing an optimal partition strategy objective function according to the multi-dimensional data, and setting time constraint and power flow constraint;
solving by adopting a particle swarm algorithm, and randomly initializing the value and the speed of each particle by setting the number N of the particles, the maximum iteration times, the maximum value and the minimum value of the inertia coefficient and a learning factor in a particle iteration formula to obtain a partitioning scheme;
and taking the partition schemes which are not independent of each other as alternative schemes, taking the objective function value as an attribute, and finally adopting a grey multi-attribute decision method to perform decision analysis, wherein the scheme with the highest score is the optimal partition scheme.
2. The high-elasticity multi-dimensional intelligent partitioning method for the urban power grid according to claim 1, wherein the optimal partitioning strategy objective function is as follows:
in the formula: p is the number of the designated system partitions and is determined by the number of the units with the self-starting capability; na (p) denotes a set of systems formed of a number of partitions with p; NA (p)kRepresents the k-th system formed by p partitions; NLiRepresents the set of overhead lines introduced in the ith zone in order to recover all the generators; NGiRepresenting a recovery unit set in the ith zone; NDiRepresenting a set of loads within the ith zone;
representing charging reactive power of a jth overhead line in an ith partition;
recovering the capacity of the unit for the g station in the ith partition;
the weight of the mth load in the ith partition;
the total number of lines required to be input from the mth load in the ith partition to the nearest generator set in the partition; the coefficient c represents the degree of importance to these two objectives.
3. The method according to claim 2, wherein the time constraint comprises:
for s ═ 1,2, ·, k partitions:
0＜Trj＜Tmcj,j＝1,2,...,Nsg
in the formula: t isrjObtaining a time interval from zero time to power supply starting for the unit j; t ismcjThe unit starting time limit of the allowance is considered; n is a radical ofsgThe number of crew nodes included in the partition s.
4. The method according to claim 2, wherein the power flow constraint comprises:
for s ═ 1,2, ·, k partitions:
Pi≤Pimax,i＝1,2,...,Ls
in the formula: n is a radical ofsnRecovering the number of nodes contained in the net rack for the partition; u shapeiIs the node voltage; piThe active power flowing through the branch i; pimaxThe maximum allowed power for branch i.
5. The highly-elastic multi-dimensional intelligent partitioning method for the urban power grid according to claim 4, wherein sensitivity analysis is adopted to adjust the output power and the load level of the generator for the scheme that the generated power flow exceeds the limit, and if the adjustment amount is within an allowable range, the scheme is still considered to be feasible; otherwise, abandoning the scheme of out-of-limit trend.
6. The high-elasticity multi-dimensional intelligent partitioning method for the urban power grid according to claim 1, wherein the solving by the particle swarm algorithm comprises:
initializing a population: randomly generating an initial population according to a designed coding mode, wherein each particle represents a group of partition schemes, and calculating an adaptive value;
constructing an external file: adding non-inferior particles into an external file based on a Pareto domination relation and by combining the adaptive value of each particle;
update speed and position: the updating of the particle positions adopts a binary updating formula, in the speed updating, the required individual optimal positions are taken from the historical non-inferior individual positions of the particles, and the overall optimal positions are taken from the particle positions of the sparse distribution area in the external file by combining the self-adaptive grid method and the roulette method;
and (3) iteration termination conditions: and when the iteration times reach the preset maximum iteration times, stopping the calculation and outputting a result, otherwise, returning to the previous step for recalculation until the iteration termination condition is met.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111341637.3A CN114219125A (en) | 2021-11-12 | 2021-11-12 | High-elasticity urban power grid multi-dimensional intelligent partitioning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111341637.3A CN114219125A (en) | 2021-11-12 | 2021-11-12 | High-elasticity urban power grid multi-dimensional intelligent partitioning method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114219125A true CN114219125A (en) | 2022-03-22 |
Family
ID=80697142
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111341637.3A Pending CN114219125A (en) | 2021-11-12 | 2021-11-12 | High-elasticity urban power grid multi-dimensional intelligent partitioning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114219125A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023202046A1 (en) * | 2022-04-18 | 2023-10-26 | 中国南方电网有限责任公司 | Power grid partitioning method |
-
2021
- 2021-11-12 CN CN202111341637.3A patent/CN114219125A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023202046A1 (en) * | 2022-04-18 | 2023-10-26 | 中国南方电网有限责任公司 | Power grid partitioning method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wong et al. | Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm | |
| CN107887903B (en) | Micro-grid robust optimization scheduling method considering element frequency characteristics | |
| Kavousi-Fard et al. | Reliability-oriented reconfiguration of vehicle-to-grid networks | |
| CN110929964B (en) | Energy-storage-containing power distribution network optimal scheduling method based on approximate dynamic programming algorithm | |
| Zhang et al. | Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO | |
| CN109886446B (en) | Dynamic economic dispatching method of electric power system based on improved chaotic particle swarm algorithm | |
| El Helou et al. | Fully decentralized reinforcement learning-based control of photovoltaics in distribution grids for joint provision of real and reactive power | |
| CN106684885B (en) | Wind turbine generator system power distribution network reactive power optimization method based on multi-scene analysis | |
| CN108649605A (en) | A kind of grid-connected allowed capacity planing methods of DER based on the double-deck scene interval trend | |
| CN103150629A (en) | Dependent-chance two-layer programming model-based transmission network programming method | |
| CN105203869A (en) | Microgrid island detection method based on extreme learning machine | |
| CN113723807B (en) | Energy storage and information system double-layer collaborative planning method, device and medium | |
| CN115940294B (en) | Multi-stage power grid real-time scheduling strategy adjustment method, system, equipment and storage medium | |
| CN114219125A (en) | High-elasticity urban power grid multi-dimensional intelligent partitioning method | |
| CN116169698A (en) | Distributed energy storage optimal configuration method and system for stable new energy consumption | |
| Zhang et al. | Deep reinforcement learning for load shedding against short-term voltage instability in large power systems | |
| Li et al. | Optimization method of skeleton network partitioning scheme considering resilience active improvement in power system restoration after typhoon passes through | |
| CN109711605A (en) | A kind of polymorphic type power grid constant volume method and apparatus | |
| CN116862021B (en) | Anti-Bayesian-busy attack decentralization learning method and system based on reputation evaluation | |
| CN109888817A (en) | Position deployment and method for planning capacity are carried out to photovoltaic plant and data center | |
| CN103515964A (en) | Reactive compensation control method and reactive compensation control device | |
| Chen et al. | A Spark-based Ant Lion algorithm for parameters optimization of random forest in credit classification | |
| Haddi et al. | Improved optimal power flow for a power system incorporating wind power generation by using Grey Wolf Optimizer algorithm | |
| CN115207935B (en) | Reactive power coordination optimization method for improving transient voltage stability of voltage weak area | |
| CN116432968A (en) | Energy storage planning method and equipment for power system |
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 |