CN114583831A - Distribution automation terminal arrangement method and device - Google Patents

Distribution automation terminal arrangement method and device Download PDF

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CN114583831A
CN114583831A CN202210143348.0A CN202210143348A CN114583831A CN 114583831 A CN114583831 A CN 114583831A CN 202210143348 A CN202210143348 A CN 202210143348A CN 114583831 A CN114583831 A CN 114583831A
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distribution automation
automation terminal
information
generating
power
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梁伟宸
王亚娟
赵志宇
刘博�
李烜
王长瑞
熊健
陈宁
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State Grid Corp of China SGCC
North China Electric Power Research Institute Co Ltd
Electric Power Research Institute of State Grid Jibei Electric Power Co Ltd
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State Grid Corp of China SGCC
North China Electric Power Research Institute Co Ltd
Electric Power Research Institute of State Grid Jibei 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
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • G—PHYSICS
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    • 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
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • 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

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Abstract

The application provides a distribution automation terminal arrangement method and a distribution automation terminal arrangement device, wherein the method comprises the following steps: generating solving information of the power distribution automation terminal optimal arrangement model based on a multi-objective optimization algorithm by combining an objective function of the power distribution automation terminal optimal arrangement model and corresponding constraint condition information; the invention provides a distribution terminal optimal arrangement method based on a multi-objective optimization algorithm, which is shown by simulation model experiments to have better convergence and stability, and provides a new method and means for the optimal arrangement problem of the multi-objective distribution automatic terminal.

Description

Distribution automation terminal arrangement method and device
Technical Field
The application relates to the field of power distribution network planning and multi-objective optimization calculation, in particular to a distribution automation terminal arrangement method and device.
Background
Distribution automation is an important means for guaranteeing power supply reliability, and as a basic component unit and key equipment of a distribution automation system, the use of a distribution terminal can improve the operation reliability of a distribution network. How to reasonably configure the installation position and the type of the power distribution terminal to achieve the balance between the system reliability and the economy is an important subject for the construction of power distribution automation.
Compared with the construction of a power transmission network, the construction of the power distribution network is still lagged at present, the standardization degree of network frame planning construction is low, the automatic coverage is small, and the power supply reliability still has a further space improvement, so that the power distribution terminal needs to be optimally configured.
Disclosure of Invention
In order to solve the technical problem, the application provides the following technical scheme:
in a first aspect, the present invention provides a power distribution automation terminal arrangement method, including:
generating solving information of the power distribution automation terminal optimal arrangement model based on a multi-objective optimization algorithm by combining an objective function of the power distribution automation terminal optimal arrangement model and corresponding constraint condition information;
and generating distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimized arrangement model so as to arrange the distribution automation terminals based on the distribution automation terminal arrangement information.
In a preferred embodiment, further comprising:
and establishing the optimal arrangement model of the distribution automation terminal according to the installation configuration information of the distribution automation terminal.
In a preferred embodiment, further comprising:
and acquiring an optimized distribution model of the distribution automation terminal, an objective function of the optimized distribution model and corresponding constraint condition information.
In a preferred embodiment, further comprising:
configuring an objective function of an optimized arrangement model of the distribution automation terminal according to the configuration data and the power failure loss data of the distribution automation terminal;
and generating constraint condition information of the power distribution automation terminal optimization arrangement model by combining the reliability constraint, the system power flow constraint and the system safe operation constraint of the power distribution network.
In a preferred embodiment, the configuring data includes equipment investment information, equipment quantity information, equipment service life data, power utilization data per unit time of equipment, and equipment investment profitability data, and the configuring an objective function of the distribution automation terminal optimized arrangement model according to the configuration data and the power outage loss data of the distribution automation terminal includes:
generating a first function model according to the equipment investment information, the equipment quantity information, the equipment service life data and the equipment investment yield data;
generating a second function model according to the annual average power shortage of the power distribution network system, the number of the feeders in the power distribution network, the total number of users on each feeder, the service life data of the equipment and the average power failure loss data of the unit electric quantity of the system;
and generating the objective function of the power distribution automation terminal optimization arrangement model according to the first function model and the second function model.
In a preferred embodiment, further comprising:
and generating the average annual power shortage amount of the power distribution network system according to the total load on each feeder line and the average annual fault power failure time length on each feeder line.
In a preferred embodiment, the constraint condition information includes power distribution network system reliability constraint condition information, and the generating constraint condition information of the distribution automation terminal optimized arrangement model by combining the reliability constraint and the system power flow constraint of the power distribution network and the system safe operation constraint includes:
generating power supply reliability according to the annual average fault power failure time of each feeder line;
and generating reliability constraint condition information of the power distribution network system according to the power supply reliability and the reference value of the power supply reliability of the power distribution network system.
In a preferred embodiment, the constraint condition information includes system power flow constraint condition information, and the generating constraint condition information of the distribution automation terminal optimized arrangement model by combining the reliability constraint and the system power flow constraint of the distribution network and the system safe operation constraint includes:
and generating system load flow constraint condition information according to a head node set, a tail node set, first-section active power, first-section reactive power, voltage amplitude of each node, active power net injection of each node, reactive power net injection of each node, equivalent resistance of each branch and equivalent reactance of each branch in the power grid.
In a preferred embodiment, the constraint condition information includes system safe operation constraint condition information, and the system safe operation constraint condition information includes transformer capacity constraint condition information, line voltage capacity constraint condition information, and node voltage amplitude constraint condition information; the generating of the constraint condition information of the distribution automation terminal optimization layout model by combining the reliability constraint, the system power flow constraint and the system safe operation constraint of the distribution network comprises the following steps:
generating transformer capacity constraint condition information according to the active power and the reactive power flowing through each transformer and the allowable capacity of each transformer;
generating line capacity constraint condition information according to the active power and the reactive power flowing through each line and the allowable capacity of each line;
and generating node voltage amplitude constraint condition information according to the active power and the reactive power flowing through each node and the voltage reference value of each node.
In a preferred embodiment, the multi-objective optimization algorithm includes an NSGA-II algorithm, and the generating solution information of the distribution automation terminal optimized arrangement model based on the multi-objective optimization algorithm by combining an objective function of the distribution automation terminal optimized arrangement model and corresponding constraint condition information includes:
coding each segmented line in the power distribution network to generate line information of each segmented line, wherein the line information comprises line codes and power distribution terminal configuration information corresponding to each line code;
randomly generating an initial parent population with a preset population scale according to the line information, wherein each individual in the initial parent population corresponds to distribution automation terminal arrangement information;
calculating an adaptive value corresponding to each individual by combining the target function and the constraint condition information;
performing an iterative operation, wherein the iterative operation comprises determining a non-dominant solution set in all individuals according to an adaptive value of each individual, assigning an initial non-dominant order to all the individuals in the non-dominant solution set, deleting all the individuals in the non-dominant solution set from the initial parent population to obtain an iterative population, determining the non-dominant solution set in the iterative population, and assigning an iterative non-dominant order to the non-dominant solution set in the iterative population until the initial parent population is split into a plurality of species layers, wherein each species layer comprises the same non-dominant order;
generating crowdedness of all individuals according to each kind of group layer and the corresponding non-dominant sequence;
and generating solving information of the distribution automation terminal optimization arrangement model according to the crowdedness of all individuals.
In a preferred embodiment, the generating the crowdedness of all individuals according to each seed group level and the corresponding non-dominant order includes:
initializing individual crowdedness of each individual in each kind group layer;
and sequencing all the bodies in the same group layer according to a set objective function value in an ascending order to generate a congestion degree arrangement, and setting the congestion degree at the edge of the congestion degree arrangement as a set congestion degree.
In a preferred embodiment, the generating solution information of the distribution automation terminal optimized arrangement model according to the crowdedness of all individuals includes:
generating the crowding distance of each individual in various group layers according to the crowding degree arrangement corresponding to each group layer, the set objective function value corresponding to each individual adjacent to each individual in the crowding degree arrangement in front and back, and the maximum value and the minimum value in the set objective function;
screening pairing individuals from the initial parent population according to a competitive bidding competition selection mechanism to form a plurality of individual pairs, performing cross operation and mutation operation on each individual pair to generate a plurality of offspring populations, and combining the initial parent population and all the offspring populations to generate a population set;
screening updated parent population from the population set according to elite retention criteria, sequentially putting each population layer into the updated parent population according to the non-dominant order size of each species layer in the initial parent population until the current updated parent population size exceeds a set threshold when putting into the next population layer, sequentially putting each individual in the next population layer into the updated parent population according to congestion distance until the population number reaches the set threshold, and generating a final parent population;
performing the iterative operation on the final parent population until the final parent population is split into a plurality of final seed group layers, wherein each final seed group layer comprises the same non-dominant order;
and generating solving information of the power distribution automation terminal optimization arrangement model according to the non-dominant order of each final seed group layer.
In a preferred embodiment, the generating of the distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimized arrangement model includes:
generating a final objective function value expression function by combining the weight values of all objective functions;
and generating distribution automation terminal arrangement information according to the final objective function value expression function and the solution information of the distribution automation terminal optimization arrangement model.
In a second aspect, the present invention further provides a distribution automation terminal arrangement device comprising:
the solving information generation module is used for generating solving information of the distribution automation terminal optimal arrangement model based on a multi-objective optimization algorithm by combining an objective function of the distribution automation terminal optimal arrangement model and corresponding constraint condition information;
and the distribution automation terminal arrangement module is used for generating distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimized arrangement model so as to arrange the distribution automation terminals based on the distribution automation terminal arrangement information.
In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the distribution automation terminal arrangement method when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the distribution automation terminal arrangement method described.
According to the technical scheme, the invention provides the distribution terminal optimal arrangement method based on the multi-objective optimization algorithm, and simulation model experiments show that the method has better convergence and stability, and provides a new method and means for the multi-objective distribution terminal optimal arrangement problem.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, 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 schematic flow chart of a power distribution automation terminal arrangement method in an embodiment of the present application.
Fig. 2 is a flowchart of a power distribution automation terminal optimization layout solving process based on the NSGA-ii algorithm in the embodiment of the present application.
Fig. 3 is a schematic diagram of an embodiment of an IEEE33 node in the present application.
Fig. 4 is a schematic structural diagram of a distribution automation terminal arrangement device in an embodiment of the present application.
Fig. 5 is a schematic structural diagram of an electronic device in the embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but 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 application.
At present, in order to solve the problem of optimal configuration of the power distribution terminal, a solution algorithm is also infinite. The method is based on a mathematical programming theory, and comprises a linear programming method, a nonlinear programming method, a quadratic programming method, a dynamic programming method and the like, wherein the method is strong in robustness, convergence of the algorithm is mathematically guaranteed, but the method is weak in response to a non-smooth function, and a global optimal solution of a non-convex problem cannot be found; secondly, modern heuristic algorithms such as genetic algorithm, particle swarm algorithm, simulated annealing algorithm, differential evolution algorithm and the like are not limited by non-convex and non-smooth problems in global optimization, but are very sensitive to the setting of algorithm parameters, and premature convergence problems easily cause the algorithm to fall into local optimization.
Based on the above, the present application provides a distribution automation terminal arrangement device for implementing the distribution automation terminal arrangement method provided in one or more embodiments of the present application, wherein the distribution automation terminal arrangement device may generate solution information of a distribution automation terminal optimized arrangement model based on a multi-objective optimization algorithm in combination with an objective function of the distribution automation terminal optimized arrangement model and corresponding constraint condition information; and then generating distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimized arrangement model so as to arrange the distribution automation terminals based on the distribution automation terminal arrangement information.
It is to be understood that the distribution automation terminal arrangement may comprise a smartphone, a tablet electronic device, a portable computer, a desktop computer, a Personal Digital Assistant (PDA) or the like.
The following embodiments and application examples are specifically and respectively described.
The present application provides an embodiment of a distribution automation terminal arrangement method, and referring to fig. 1, the distribution automation terminal arrangement method specifically includes the following contents:
step S1: and generating solving information of the distribution automation terminal optimal arrangement model based on a multi-objective optimization algorithm by combining an objective function of the distribution automation terminal optimal arrangement model and corresponding constraint condition information.
Step S2: and generating distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimized arrangement model so as to arrange the distribution automation terminals based on the distribution automation terminal arrangement information.
From the above description, the distribution automation terminal arrangement method provided in the embodiment of the present application is based on the distribution terminal optimized arrangement of the multi-objective optimization algorithm, and simulation model experiments show that the method has better convergence and stability, and provides a new method and means for the multi-objective distribution automation terminal optimized arrangement problem.
In the present invention, the distribution automation terminal optimized arrangement model may be generated in advance, or may be generated on line, and the present invention is not limited thereto, and in one embodiment, the steps of the present invention include a generation process of the distribution automation terminal optimized arrangement model, that is:
the distribution automation terminal arrangement method further comprises:
and establishing the optimal arrangement model of the distribution automation terminal according to the installation configuration information of the distribution automation terminal.
In some embodiments, the invention can directly obtain the optimal arrangement model of the distribution automation terminal, the objective function of the optimal arrangement model and the corresponding constraint condition information, and can also generate the optimal arrangement model of the distribution automation terminal, the objective function of the optimal arrangement model and the corresponding constraint condition information according to the situation.
For example, in some embodiments, the distribution automation terminal placement method further comprises:
and acquiring an optimized distribution model of the distribution automation terminal, an objective function of the optimized distribution model and corresponding constraint condition information.
The model, the objective function, and the constraint condition information in this embodiment may be obtained through network acquisition or user input, and the present invention is not limited thereto.
Further, the method of the present invention further includes:
s01: configuring an objective function of an optimized arrangement model of the distribution automation terminal according to the configuration data and the power failure loss data of the distribution automation terminal;
s02: and generating constraint condition information of the power distribution automation terminal optimization arrangement model by combining the reliability constraint, the system power flow constraint and the system safe operation constraint of the power distribution network.
In this embodiment, the configuration of the objective function may be obtained by:
the configuration data includes equipment investment information, equipment quantity information, equipment service life data, power utilization data in unit time of equipment and equipment investment profitability data, and the objective function of the distribution automation terminal optimized arrangement model is configured according to the configuration data and the power failure loss data of the distribution automation terminal, and the method includes the following steps:
generating a first function model according to the equipment investment information, the equipment quantity information, the equipment service life data and the equipment investment yield data;
generating a second function model according to the annual average power shortage of the power distribution network system, the number of the feeders in the power distribution network, the total number of users on each feeder, the service life data of the equipment and the average power failure loss data of the unit electric quantity of the system;
and generating the objective function of the power distribution automation terminal optimization arrangement model according to the first function model and the second function model.
Further, the method also comprises the following steps:
and generating the average annual power shortage amount of the power distribution network system according to the total load on each feeder line and the average annual fault power failure time length on each feeder line.
The above steps of the present invention are described in detail below with specific examples.
The optimal arrangement of the distribution automation terminal considered by the invention aims to determine the installation position of the optimal distribution terminal, and the comprehensive cost of the system is lowest on the premise of meeting the requirement of the reliability of the system. The model ignores the operation and maintenance cost of the equipment, and only considers the investment configuration of the three-remote distribution automation terminal. Therefore, the model objective function designed by the invention is two objectives of equipment investment cost and power failure loss cost within the service life of the equipment. The objective function expression of the model multi-objective optimization is shown in formulas (1) to (3):
minf＝{f1,f2} (1)
Figure BDA0003507551390000081
Figure BDA0003507551390000082
in the formula (f)1Representing an equipment investment cost objective function, namely a first function model; f. of2Representing a power failure loss cost objective function, namely a second function model; n is a radical of3Representing the number of the 'three remote' terminals; p3Representing the investment present price of the 'three remote' terminal; s represents the economic service life of the equipment; r represents the recovery of investment; c. CLThe average power failure loss cost of the unit electric quantity of the system is represented; n is a radical ofeiRepresenting the total number of subscribers on feeder i; n represents the number of feeders in the distribution network; AENS is the average annual power shortage of a power distribution network system, and the expressions are shown as (4) to (5):
AENSi＝LsiSAIDIi/Nei (4)
Figure BDA0003507551390000083
in the formula, LSiRepresenting the total amount of load on feeder i; SAIDIiThe average annual fault outage time of the feeder i is represented by the expression (6):
Figure BDA0003507551390000084
in the formula, NTiThe total number of the load nodes on the feeder line i; t isijAnd the annual average fault power failure time of the node j on the feeder i is represented.
The generation of constraint information according to the present invention will be described in detail below.
In some embodiments, the constraint information includes reliability constraint information of the power distribution network system, and the generation of the reliability constraint information may be performed by power supply reliability, for example, the following steps may be included:
generating power supply reliability according to the annual average fault power failure time of each feeder line;
and generating reliability constraint condition information of the power distribution network system according to the power supply reliability and the reference value of the power supply reliability of the power distribution network system.
Specifically, the details will be described with reference to specific examples given below.
1) And regarding the reliability constraint of the power distribution network system, the power supply reliability ASAI of the system meets the requirement of the power distribution network system.
ASAI≤ASAIth (7)
In the formula, ASAIthA reference value representing the power supply reliability of the power distribution network system; the expression of the power supply reliability ASAI is shown in formula (8):
ASAI＝(1-SAIDI/8760)×100％ (8)
in the formula, SAIDI represents the average annual fault power failure time of the whole N feeders in the power distribution network system, and SAIDI represented by formula (6)iThe SAIDI is expressed as shown in formula (9):
Figure BDA0003507551390000091
in addition, in another embodiment, the constraint condition information includes system power flow constraint condition information, and in this embodiment, the following steps may be adopted to generate the system power flow constraint condition information:
and generating system load flow constraint condition information according to a head node set, a tail node set, first-section active power, first-section reactive power, voltage amplitude of each node, active power net injection of each node, reactive power net injection of each node, equivalent resistance of each branch and equivalent reactance of each branch in the power grid.
For example, with respect to system power flow constraints, a power distribution network system to be optimized needs to satisfy a power flow convergence condition.
And the input data of the simulation model should meet the load flow convergence condition of the power grid. The invention only considers the radiation type power distribution network, and for any node j, the Distflow form of the power flow equation is as follows:
Figure BDA0003507551390000092
Figure BDA0003507551390000093
Figure BDA0003507551390000094
in the formula, the set u (j) represents a head end node set of a branch with j as a tail end node in the power grid; set v (j) represents the set of end nodes for a branch with j as the head-end node; pijAnd QijThe active power and the reactive power of the first section of the branch ij are represented; u shapeiRepresents the voltage amplitude of the node i; piAnd QiRepresenting the net injection of active power and reactive power at node i; r isijAnd xijRepresenting the equivalent resistance and equivalent reactance of branch ij.
Wherein, P in the formulas (10) and (11)iAnd QiThe method comprises the following steps:
Figure BDA0003507551390000101
in the formula, Pi,DGAnd Qi,DGRespectively representing active power and reactive power of a node i for hooking a DG; pi,LAnd Qi,LRespectively representing active power and reactive power of a node i hitching load; qi.comAnd the active power and the reactive power of the reactive compensation equipment hung on the node i are shown.
In addition, the constraint condition information in the invention further comprises system safe operation constraint condition information, wherein the system safe operation constraint condition information comprises transformer capacity constraint condition information, line voltage capacity constraint condition information and node voltage amplitude constraint condition information; the generating of the constraint condition information of the distribution automation terminal optimization layout model by combining the reliability constraint, the system power flow constraint and the system safe operation constraint of the distribution network comprises the following steps:
generating transformer capacity constraint condition information according to the active power and the reactive power flowing through each transformer and the allowable capacity of each transformer;
generating line capacity constraint condition information according to the active power and the reactive power flowing through each line and the allowable capacity of each line;
and generating node voltage amplitude constraint condition information according to the active power and the reactive power flowing through each node and the voltage reference value of each node.
Specifically, with respect to system safe operation constraints, the power distribution network to be optimized needs to satisfy line and transformer capacity constraints and node voltage amplitude constraints, namely:
Figure BDA0003507551390000102
Figure BDA0003507551390000103
Figure BDA0003507551390000104
in the formula, omegaSS、ΩLAnd ΩBRespectively representing equipment sets of a transformer, lines and nodes in the power distribution network system; pSS,j、QSS,jAnd SSmax,jRespectively representing the active power and the reactive power flowing through the transformer j and the allowed capacity of the transformer j; pL,j、QL,jAnd SLmax,jRespectively representing the active power and the reactive power flowing through the line j and the allowable capacity of the line j; vjRepresents the voltage magnitude of node j; vNRepresents a voltage reference value; delta. for the preparation of a coatingVIndicating the percentage of allowable voltage deviation.
Therefore, it can be seen that the equations (7), (10) and (16) provide constraint condition information of the distribution automation terminal optimization layout model for the present invention.
As will be described in detail below with respect to step S1 of the present invention, in some embodiments, the multi-objective optimization algorithm includes the NSGA-II algorithm, which is one of the most popular multi-objective genetic algorithms, and which reduces the complexity of non-bad ordering genetic algorithms, has the advantages of fast operation speed and good convergence of solution set, and becomes the basis for the performance of other multi-objective optimization algorithms.
In the present invention, step S1, namely, the generating solution information of the distribution automation terminal optimized layout model based on the multi-objective optimization algorithm by combining the objective function of the distribution automation terminal optimized layout model and the corresponding constraint condition information includes:
s10: coding each segmented line in the power distribution network to generate line information of each segmented line, wherein the line information comprises line codes and power distribution terminal configuration information corresponding to each line code;
s11: randomly generating an initial parent population with a preset population scale according to the line information, wherein each individual in the initial parent population corresponds to distribution automation terminal arrangement information;
s12: calculating an adaptive value corresponding to each individual by combining the target function and the constraint condition information;
s13: performing an iterative operation, wherein the iterative operation comprises determining a non-dominant solution set in all individuals according to an adaptive value of each individual, assigning an initial non-dominant order to all the individuals in the non-dominant solution set, deleting all the individuals in the non-dominant solution set from the initial parent population to obtain an iterative population, determining the non-dominant solution set in the iterative population, and assigning an iterative non-dominant order to the non-dominant solution set in the iterative population until the initial parent population is split into a plurality of species layers, wherein each species layer comprises the same non-dominant order;
s14: generating crowdedness of all individuals according to each kind of group layer and the corresponding non-dominant sequence;
s15: and generating solving information of the distribution automation terminal optimization arrangement model according to the crowdedness of all individuals.
The generating of the individual congestion degrees for all the individuals according to each of the seed group levels and the corresponding non-dominant rank includes:
initializing individual crowdedness of each individual in each kind group layer;
and sorting the individuals in the same group layer in an ascending order according to set objective function values to generate a congestion degree arrangement, and setting the congestion degree at the edge of the congestion degree arrangement as a set congestion degree.
Further, step S15 includes:
s151, generating the crowding distance of each individual in various group layers according to the crowding degree arrangement corresponding to each group layer, the set objective function value corresponding to each individual adjacent to the individual in front of and behind the crowding degree arrangement, and the maximum value and the minimum value in the set objective function;
s152, screening pairing individuals from the initial parent population according to a competitive bidding competition selection mechanism to form a plurality of individual pairs, performing cross operation and mutation operation on each individual pair to generate a plurality of offspring populations, and combining the initial parent population and all the offspring populations to generate a population set;
s153, screening updated parent population from the population set according to elite retention criteria, sequentially putting each population layer into the updated parent population according to the non-dominant order size of each species group layer in the initial parent population until the current updated parent population size exceeds a set threshold when putting into the next species group layer, sequentially putting each body in the next species group layer into the updated parent population according to congestion distance until the population number reaches the set threshold, and generating the final parent population;
s154, the iteration operation is carried out on the final parent population until the final parent population is split into a plurality of final species group layers, wherein each final species group layer comprises the same non-dominant order;
and S155, generating solving information of the distribution automation terminal optimization arrangement model according to the non-dominant order of each final species group layer.
The above steps are specifically described below with reference to fig. 2, and are further described as follows:
1) when the optimized distribution model of the power distribution terminal is solved by adopting an NSGA-II algorithm, the coding problem needs to be solved firstly. The coding scheme for each segmented line is used herein as follows:
L＝{No.D1 D2}
wherein L represents a segment line; no. represents a line number; d1 and D2 respectively indicate whether the two ends of the line are provided with power distribution terminals, if yes, the value is 1, and if not, the value is 0.
2) Randomly generating an initial parent population P with a population size of M according to a designed genetic coding modetEach individual represents an optimal arrangement scheme of the distribution automation terminal, and adaptive values of the objective functions are calculated according to different schemes. It should be noted that the generated initial population needs to satisfy all the constraint condition information mentioned in S2.
3) Performing non-dominant ordering, wherein concepts of dominant solution and non-dominant solution are introduced, if solution x1Is an objective function fjIf the following conditions are satisfied, the solution x is called1Dominating solution x2。
Figure BDA0003507551390000121
Figure BDA0003507551390000122
In the formula, x1Namely the dominant solution; x is the number of2Is a non-dominant solution.
Non-dominated sorting is a round-robin adaptive value-ranking process. Firstly, finding out a non-dominant solution set in a population according to an equation (17) and an equation (18), and marking the solution set as a first non-dominant layer F1Will F1All individuals were assigned non-dominant order irank1 (wherein: i)rankIs the non-dominant order value of individual i) and is removed from the overall population; then, continuously finding out a non-dominant solution set in the rest population, and marking as a second non-dominant layer F2Individuals are assigned a non-dominant order i rank2; this is done until the entire population is stratified, with individuals in the same stratification having the same non-dominant order irank。
4) The crowdedness of all individuals is calculated. The crowding distance for individual i is the distance between 2 individuals i +1 and i-1 that are spatially adjacent to individual i in the target space. The steps of calculating the congestion degree are as follows:
(ii) pairs having the same non-dominant order irankThe individual of (1) initializes the congestion degree, and makes the congestion degree i of any individual id＝0。
② for the same non-dominant order irankIn ascending order of the mth objective function value.
Let the individuals on the sorting edge have the selection advantage, and set the crowdedness at the two edges to ∞.
Fourthly, calculating the crowdedness i of the individual id：
Figure BDA0003507551390000131
In the formula (f)m(i +1) and fm(i-1) representing the mth objective function values of the individuals i +1 and i-1, respectively; f. ofm maxAnd fm minRespectively, the maximum and minimum values in the mth set of objective function values.
Fifthly, repeating the step II for different target functions-obtaining a final crowding distance i for the individual id。
By preferentially selecting individuals with large crowding distances, the calculation results can be distributed more uniformly in the target space so as to maintain the diversity of the population.
5) From parent population P by competitive contest selection mechanismtSelecting M/2 individuals to form a pairing population, and performing cross and mutation operations on the pairing population to generate a child population D with the size of NtThe parent population PtAnd progeny population DtMerging into a population R of size 2Mt。
6) From population R according to elite retention strategytTo generate a new parent population Pt+1. First in non-dominant order irankSequentially putting the whole layer of population into P from low to hight+1Until P appears when a layer of non-dominant order is placedt+1The size exceeds the population size limit N; finally according to each layer of non-dominant order irankIn the order of the individual crowding distance from large to small, continue to fill in Pt+1And terminating until the population number reaches N.
7) And returning to the step 3) until the termination condition is met, and obtaining a Pareto optimal solution set of the distribution automation terminal multi-objective optimal arrangement model.
Furthermore, in the present invention, since the aforementioned objective function includes a first function model and a second function model, that is, the objective function includes two, the generating of the distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimized arrangement model includes:
generating a final objective function value expression function by combining the weight values of all objective functions;
and generating distribution automation terminal arrangement information according to the final objective function value expression function and the solution information of the distribution automation terminal optimization arrangement model.
Taking the embodiments of the two objective functions as examples, for the solved Pareto optimal solution set of the distribution automation terminal multi-objective optimal arrangement model, a proper optimal solution selection strategy is formulated, and a most proper terminal arrangement party is selectedA method for preparing a medical liquid. The method is used for Pareto leading edge P of two target functions1And P2Respectively endowing with weight values, and selecting an optimal arrangement scheme for the target according to the minimum final objective function value:
P＝λ1P1+λ2P2 (20)
wherein P represents the final objective function value; lambda [ alpha ]1And λ2Respectively represent P1And P2The weight of (2) is taken.
The optimization solving process of the distribution automation terminal optimization arrangement model based on the NSGA-II algorithm provided by the invention is specifically described by taking an IEEE33 node as a specific embodiment.
A schematic diagram of an embodiment of an IEEE33 node is shown in fig. 3. In the figure, a node 0 is a 110kV node, the voltage levels of nodes 1-33 are 10kV, and the nodes 0-1 are connected through 110/10kV transformers. The line connecting the 1-18 nodes is a trunk line; the lines connecting nodes 2-22, the lines connecting nodes 3-25 and the lines connecting nodes 6-33 are large branch lines. The feeder automation mode is an adaptive comprehensive feeder automation mode. The line parameters and the node load data adopt SCADA data acquired in 2014 of a Hainan 10kV power distribution network.
The investment current price P of the 'three remote' terminal is assumed in the calculation3Is 6 ten thousand yuan/group; the investment recovery rate r is 10 percent; the economic service life S of the equipment is 10 years; average power loss cost c of system unit electricityLIs 25 yuan/(kW/h).
In the calculation iteration process, the model needs to meet power supply reliability ASAI constraint and power flow convergence constraint. In the calculation of the calculation example, the model is assumed to be a C-type load, and the power supply reliability ASAI requirement reaches 99.897%. The positions of the 'three remote' distribution terminals to be installed are the head and tail nodes of each subsection line of the trunk line, namely two ends of the nodes 1-18. The probability of line faults is assumed to be 0.1 time/km year in the calculation example, faults occurring in 10 years in the Monte Carlo simulation random simulation model are based in each algorithm iteration process, and the power failure loss cost in 10 years is calculated.
Basic parameters of the NSGA-II algorithm are set to be 50 of population scale; the gene digit is 10; the crossover probability is 0.8; the variation probability is 0.05; a total of 300 iterations.
The Pareto optimal solution set solved by the NSGA-II algorithm and the corresponding Pareto leading edge are shown in the table 1.
TABLE 1
Figure BDA0003507551390000141
As can be seen from the above table, there is only one Pareto optimal solution set element in this embodiment, so that the optimal solution selection operation of S4 is not required, and the scheme in the table is the final planning scheme. 4 power distribution terminals are configured in the scheme, and the planning positions of the power distribution terminals are {1-, 5-, 9-, 13- }. Where "-" indicates that the terminal is installed at the head end of the node corresponding in number. At this time, the value of the power supply reliability ASAI is 99.041%, which meets the reliability requirement of the power distribution network.
The above embodiment implements establishment of a distribution network model based on the power system simulation software DIgSILENT, including a primary side model, a secondary side relay protection model, and a feeder automation action logic model (adaptive integrated feeder automation). And calculating the system power flow in the model and setting transient fault simulation. In this embodiment, the power failure time of each load and the power failure area of the whole power distribution network under the simulated fault condition of the power distribution network can be obtained by simulating the power distribution network fault in DIgSILENT simulation software, setting feeder automation setting parameters, and then performing power flow and transient simulation calculation and feeder automation simulation on the power distribution network. And outputting power distribution network data from DIgSILENT by using a designed feeder automation data interaction framework, uniformly processing the software output data outside simulation software based on Python language to obtain high-order data such as reliability indexes and the like, and finally calculating each individual objective function value.
From a software level, the present application provides an embodiment of a distribution automation terminal arrangement device for executing all or part of the contents of the distribution automation terminal arrangement method, see fig. 4, which specifically includes the following contents:
the solution information generation module 10 is used for generating solution information of the distribution automation terminal optimal arrangement model based on a multi-objective optimization algorithm by combining an objective function of the distribution automation terminal optimal arrangement model and corresponding constraint condition information;
and the arrangement module 20 is used for generating distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimal arrangement model so as to arrange the distribution automation terminals based on the distribution automation terminal arrangement information.
According to the technical scheme, the distribution automation terminal arrangement device provided by the application has the advantages that the distribution terminal is optimally arranged through a multi-objective optimization algorithm, and simulation model experiments show that the method has better convergence and stability, and a new method and means are provided for the multi-objective distribution automation terminal optimal arrangement problem.
Based on the same inventive concept, in a preferred embodiment, the method further comprises:
and the model establishing module is used for establishing the optimal arrangement model of the distribution automation terminal according to the installation configuration information of the distribution automation terminal.
Based on the same inventive concept, in a preferred embodiment, the method further comprises:
and the acquisition module acquires the optimal arrangement model of the distribution automation terminal, the objective function of the optimal arrangement model and corresponding constraint condition information.
Based on the same inventive concept, in a preferred embodiment, the method further comprises:
the objective function configuration module is used for configuring an objective function of the power distribution automation terminal optimization arrangement model according to the configuration data and the power failure loss data of the power distribution automation terminal;
and the constraint condition information configuration module is used for generating constraint condition information of the power distribution automation terminal optimization arrangement model by combining the reliability constraint and the system power flow constraint of the power distribution network and the system safe operation constraint.
Based on the same inventive concept, in a preferred embodiment, the configuration data includes device investment information, device quantity information, device service life data, power utilization data of a device in unit time, and device investment profitability data, and the objective function configuration module includes:
a first function model generating unit for generating a first function model according to the equipment investment information, the equipment quantity information, the equipment service life data and the equipment investment yield data;
the second function model generating unit is used for generating a second function model according to the annual average power shortage of the power distribution network system, the number of the feeder lines in the power distribution network, the total number of users on each feeder line, the service life data of equipment and the average power failure loss data of the unit electric quantity of the system;
and the objective function generating unit is used for generating the objective function of the power distribution automation terminal optimization arrangement model according to the first function model and the second function model.
Based on the same inventive concept, in a preferred embodiment, the method further comprises:
and the average power shortage generation module is used for generating the average annual power shortage of the power distribution network system according to the total load on each feeder line and the average annual fault power failure time length on each feeder line.
Based on the same inventive concept, in a preferred embodiment, the constraint condition information includes power distribution network system reliability constraint condition information, and the constraint condition information configuration unit is specifically configured to:
generating power supply reliability according to the annual average fault power failure time of each feeder line;
and generating reliability constraint condition information of the power distribution network system according to the power supply reliability and the reference value of the power supply reliability of the power distribution network system.
Based on the same inventive concept, in a preferred embodiment, the constraint condition information includes system power flow constraint condition information, and the constraint condition information configuration unit is specifically configured to:
and generating system load flow constraint condition information according to a head node set, a tail node set, first-section active power, first-section reactive power, voltage amplitude of each node, active power net injection of each node, reactive power net injection of each node, equivalent resistance of each branch and equivalent reactance of each branch in the power grid.
Based on the same inventive concept, in a preferred embodiment, the constraint condition information includes system safe operation constraint condition information, which includes transformer capacity constraint condition information, line voltage capacity constraint condition information, and node voltage amplitude constraint condition information; the constraint condition information configuration unit is specifically configured to:
generating transformer capacity constraint condition information according to the active power and the reactive power flowing through each transformer and the allowable capacity of each transformer;
generating line capacity constraint condition information according to the active power and the reactive power flowing through each line and the allowable capacity of each line;
and generating node voltage amplitude constraint condition information according to the active power and the reactive power flowing through each node and the voltage reference value of each node.
Based on the same inventive concept, in a preferred embodiment, the multi-objective optimization algorithm includes an NSGA-II algorithm, and the solving module includes:
the coding unit is used for coding each segmented line in the power distribution network to generate line information of each segmented line, wherein the line information comprises line codes and power distribution terminal configuration information corresponding to each line code;
the initial parent population generating unit is used for randomly generating an initial parent population with a preset population scale according to the line information, wherein each individual in the initial parent population corresponds to distribution automation terminal arrangement information;
the adaptive value generating unit is used for calculating the adaptive value corresponding to each individual by combining the target function and the constraint condition information;
an iteration unit, configured to perform an iteration operation, where the iteration operation includes determining a non-dominant solution set in all individuals according to an adaptation value of each individual, assigning an initial non-dominant order to all individuals in the non-dominant solution set, and deleting all individuals in the non-dominant solution set from the initial parent population to obtain an iteration population, determining a non-dominant solution set in the iteration population, and assigning an iteration non-dominant order to the non-dominant solution set in the iteration population until the initial parent population is split into multiple species layers, where each species layer includes the same non-dominant order;
a congestion degree generation unit which generates congestion degrees of all individuals according to each type of group level and the corresponding non-dominant order;
and the solving information generating unit is used for generating solving information of the distribution automation terminal optimized arrangement model according to the crowdedness of all individuals.
Based on the same inventive concept, in a preferred embodiment, the congestion degree generating unit is specifically configured to:
initializing individual crowdedness of each individual in each kind group layer;
and sorting the individuals in the same group layer in an ascending order according to set objective function values to generate a congestion degree arrangement, and setting the congestion degree at the edge of the congestion degree arrangement as a set congestion degree.
Based on the same inventive concept, in a preferred embodiment, the solving unit is specifically configured to:
generating the crowding distance of each individual in various group layers according to the crowding degree arrangement corresponding to each group layer, the set objective function value corresponding to each individual adjacent to each individual in the crowding degree arrangement in front and back, and the maximum value and the minimum value in the set objective function;
screening pairing individuals from the initial parent population according to a competitive bidding competition selection mechanism to form a plurality of individual pairs, performing cross operation and mutation operation on each individual pair to generate a plurality of offspring populations, and combining the initial parent population and all the offspring populations to generate a population set;
screening updated parent population from the population set according to elite retention criteria, sequentially putting each population layer into the updated parent population according to the non-dominant order size of each species layer in the initial parent population until the current updated parent population size exceeds a set threshold when putting into the next population layer, sequentially putting each individual in the next population layer into the updated parent population according to congestion distance until the population number reaches the set threshold, and generating a final parent population;
performing the iterative operation on the final parent population until the final parent population is split into a plurality of final seed group layers, wherein each final seed group layer comprises the same non-dominant order;
and generating solving information of the distribution automation terminal optimization arrangement model according to the non-dominant order of each final species group layer.
Based on the same inventive concept, in a preferred embodiment, the solving module is further configured to:
combining the weight values of the objective functions to generate a final objective function value expression function;
and generating distribution automation terminal arrangement information according to the final objective function value expression function and the solution information of the distribution automation terminal optimization arrangement model.
From a hardware level, the present application provides an embodiment of an electronic device for implementing all or part of the contents in the power distribution automation terminal arrangement method, where the electronic device specifically includes the following contents:
fig. 5 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 5, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 5 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In an embodiment, the distribution automation terminal arrangement functionality may be integrated into the central processor. Wherein the central processor may be configured to control:
step S1: and generating solving information of the distribution automation terminal optimal arrangement model based on a multi-objective optimization algorithm by combining an objective function of the distribution automation terminal optimal arrangement model and corresponding constraint condition information.
Step S2: and generating distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimized arrangement model so as to arrange the distribution automation terminals based on the distribution automation terminal arrangement information.
From the above description, the electronic device provided in the embodiment of the present application is optimized for the distribution terminals through a multi-objective optimization algorithm, and a simulation model experiment shows that the method has better convergence and stability, and provides a new method and means for the problem of optimized arrangement of the multi-objective distribution automation terminals.
In another embodiment, the distribution automation terminal arrangement may be configured separately from the central processor 9100, for example, the distribution automation terminal arrangement may be configured as a chip connected to the central processor 9100, and the distribution automation terminal arrangement function is implemented by the control of the central processor.
As shown in fig. 5, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 5; further, the electronic device 9600 may further include components not shown in fig. 5, which may be referred to in the art.
As shown in fig. 5, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage part 9142, the application/function storage part 9142 being used to store application programs and function programs or a flow for executing the operation of the electronic device 9600 by the central processing unit 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
Embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the distribution automation terminal arrangement method in the foregoing embodiments, where the computer-readable storage medium stores thereon a computer program that, when executed by a processor, implements all the steps of the distribution automation terminal arrangement method whose main execution subject is the distribution automation terminal arrangement device or the client, for example, the processor implements the following steps when executing the computer program:
step S1: and generating solving information of the distribution automation terminal optimal arrangement model based on a multi-objective optimization algorithm by combining an objective function of the distribution automation terminal optimal arrangement model and corresponding constraint condition information.
Step S2: and generating distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimized arrangement model so as to arrange the distribution automation terminals based on the distribution automation terminal arrangement information.
From the above description, the electronic device provided in the embodiment of the present application is optimized for the distribution terminals through a multi-objective optimization algorithm, and a simulation model experiment shows that the method has better convergence and stability, and provides a new method and means for the problem of optimized arrangement of the multi-objective distribution automation terminals.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (15)

1. A distribution automation terminal arranging method characterized by comprising:
generating solving information of the power distribution automation terminal optimal arrangement model based on a multi-objective optimization algorithm by combining an objective function of the power distribution automation terminal optimal arrangement model and corresponding constraint condition information;
and generating distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimized arrangement model so as to arrange the distribution automation terminals based on the distribution automation terminal arrangement information.
2. The distribution automation terminal placement method of claim 1, further comprising:
and establishing the optimal arrangement model of the distribution automation terminal according to the installation configuration information of the distribution automation terminal.
3. The distribution automation terminal placement method of claim 1, further comprising:
configuring an objective function of an optimized arrangement model of the distribution automation terminal according to the configuration data and the power failure loss data of the distribution automation terminal;
and generating constraint condition information of the power distribution automation terminal optimization arrangement model by combining the reliability constraint, the system power flow constraint and the system safe operation constraint of the power distribution network.
4. The distribution automation terminal arrangement method according to claim 3, wherein the configuration data includes equipment investment information, equipment quantity information, equipment service life data, power utilization data per unit time of equipment, and equipment investment profitability data, and the configuring the objective function of the distribution automation terminal optimal arrangement model according to the configuration data and the outage loss data of the distribution automation terminal includes:
generating a first function model according to the equipment investment information, the equipment quantity information, the equipment service life data and the equipment investment yield data;
generating a second function model according to the annual average power shortage of the power distribution network system, the number of the feeders in the power distribution network, the total number of users on each feeder, the service life data of the equipment and the average power failure loss data of the unit electric quantity of the system;
and generating the objective function of the power distribution automation terminal optimization arrangement model according to the first function model and the second function model.
5. The distribution automation terminal placement method of claim 3, further comprising:
and generating the average annual power shortage amount of the power distribution network system according to the total load on each feeder line and the average annual fault power failure time length on each feeder line.
6. The distribution automation terminal arrangement method according to claim 3, wherein the constraint condition information includes distribution network system reliability constraint condition information, and the constraint condition information for generating the distribution automation terminal optimal arrangement model by combining the reliability constraint and the system flow constraint of the distribution network and the system safe operation constraint comprises:
generating power supply reliability according to the annual average fault power failure time of each feeder line;
and generating reliability constraint condition information of the power distribution network system according to the power supply reliability and the reference value of the power supply reliability of the power distribution network system.
7. The distribution automation terminal deployment method of claim 3 wherein the constraint information comprises system power flow constraint information, and wherein generating constraint information for the distribution automation terminal optimal deployment model in combination with reliability constraints and system power flow constraints of the distribution network and system safe operation constraints comprises:
and generating system load flow constraint condition information according to a head node set, a tail node set, first-section active power, first-section reactive power, voltage amplitude of each node, active power net injection of each node, reactive power net injection of each node, equivalent resistance of each branch and equivalent reactance of each branch in the power grid.
8. The distribution automation terminal arrangement method according to claim 3, wherein the constraint condition information includes system safe operation constraint condition information including transformer capacity constraint condition information, line voltage capacity constraint condition information, and node voltage amplitude constraint condition information; the generating of the constraint condition information of the distribution automation terminal optimization layout model by combining the reliability constraint, the system power flow constraint and the system safe operation constraint of the distribution network comprises the following steps:
generating transformer capacity constraint condition information according to the active power and the reactive power flowing through each transformer and the allowable capacity of each transformer;
generating line capacity constraint condition information according to the active power and the reactive power flowing through each line and the allowable capacity of each line;
and generating node voltage amplitude constraint condition information according to the active power and the reactive power flowing through each node and the voltage reference value of each node.
9. The distribution automation terminal arrangement method according to claim 2, wherein the multi-objective optimization algorithm comprises an NSGA-II algorithm, and the generating of the solution information of the distribution automation terminal optimized arrangement model based on the multi-objective optimization algorithm in combination with the objective function of the distribution automation terminal optimized arrangement model and the corresponding constraint condition information comprises:
coding each segmented line in the power distribution network to generate line information of each segmented line, wherein the line information comprises line codes and power distribution terminal configuration information corresponding to each line code;
randomly generating an initial parent population with a preset population scale according to the line information, wherein each individual in the initial parent population corresponds to distribution automation terminal arrangement information;
calculating an adaptive value corresponding to each individual by combining the target function and the constraint condition information;
performing an iterative operation, wherein the iterative operation comprises determining a non-dominant solution set in all individuals according to an adaptive value of each individual, assigning an initial non-dominant order to all the individuals in the non-dominant solution set, deleting all the individuals in the non-dominant solution set from the initial parent population to obtain an iterative population, determining the non-dominant solution set in the iterative population, and assigning an iterative non-dominant order to the non-dominant solution set in the iterative population until the initial parent population is split into a plurality of species layers, wherein each species layer comprises the same non-dominant order;
generating crowdedness of all individuals according to each kind of group layer and the corresponding non-dominant sequence;
and generating solving information of the distribution automation terminal optimization arrangement model according to the crowdedness of all individuals.
10. The distribution automation terminal placement method according to claim 9, wherein the generating the crowdedness of all individuals according to each category group level and the corresponding non-dominant order comprises:
initializing individual crowdedness of each individual in each kind group layer;
and sorting the individuals in the same group layer in an ascending order according to set objective function values to generate a congestion degree arrangement, and setting the congestion degree at the edge of the congestion degree arrangement as a set congestion degree.
11. The distribution automation terminal arrangement method according to claim 10, wherein the generating solution information of the distribution automation terminal optimal arrangement model according to the crowdedness of all individuals comprises:
generating the crowding distance of each individual in various group layers according to the crowding degree arrangement corresponding to each group layer, the set objective function value corresponding to each individual adjacent to each individual in the crowding degree arrangement in front and back, and the maximum value and the minimum value in the set objective function;
screening pairing individuals from the initial parent population according to a competitive bidding competition selection mechanism to form a plurality of individual pairs, performing cross operation and mutation operation on each individual pair to generate a plurality of offspring populations, and combining the initial parent population and all the offspring populations to generate a population set;
screening updated parent populations from the population set according to elite retention criteria, sequentially putting each population layer into the updated parent populations according to the non-dominant order size of each species layer in the initial parent populations until the current updated parent population size exceeds a set threshold value when putting into a next species layer, sequentially putting each parent into the updated parent populations according to congestion distances of each individual in the next species layer until the population number reaches the set threshold value, and generating final parent populations;
performing the iterative operation on the final parent population until the final parent population is split into a plurality of final seed group layers, wherein each final seed group layer comprises the same non-dominant order;
and generating solving information of the power distribution automation terminal optimization arrangement model according to the non-dominant order of each final seed group layer.
12. The distribution automation terminal arrangement method according to claim 9, wherein the generating of distribution automation terminal arrangement information from solution information of the distribution automation terminal optimized arrangement model comprises:
generating a final objective function value expression function by combining the weight values of all objective functions;
and generating distribution automation terminal arrangement information according to the final objective function value expression function and the solution information of the distribution automation terminal optimization arrangement model.
13. A distribution automation terminal arrangement device characterized by comprising:
the solving information generation module is used for generating solving information of the distribution automation terminal optimal arrangement model based on a multi-objective optimization algorithm by combining an objective function of the distribution automation terminal optimal arrangement model and corresponding constraint condition information;
and the distribution automation terminal arrangement module is used for generating distribution automation terminal arrangement information according to the solution information of the distribution automation terminal optimized arrangement model so as to arrange the distribution automation terminals based on the distribution automation terminal arrangement information.
14. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the power distribution automation terminal arrangement method according to any one of claims 1 to 12 when executing the program.
15. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the distribution automation terminal arranging method according to any one of claims 1 to 12.
CN202210143348.0A 2022-02-16 2022-02-16 Distribution automation terminal arrangement method and device Pending CN114583831A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115952925A (en) * 2023-03-10 2023-04-11 南京理工大学 Power distribution terminal optimal configuration method considering extreme weather

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115952925A (en) * 2023-03-10 2023-04-11 南京理工大学 Power distribution terminal optimal configuration method considering extreme weather

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