CN112564138A - Three-phase unbalanced reactive power optimization method and system thereof - Google Patents

Three-phase unbalanced reactive power optimization method and system thereof Download PDF

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CN112564138A
CN112564138A CN202011296009.3A CN202011296009A CN112564138A CN 112564138 A CN112564138 A CN 112564138A CN 202011296009 A CN202011296009 A CN 202011296009A CN 112564138 A CN112564138 A CN 112564138A
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representing
node
reactive power
phase
voltage
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张琪
马镛湛
李蒙赞
梁静
冯凯
刘自力
但唐军
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Jincheng Power Supply Co of State Grid Shanxi Electric Power Co Ltd
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Jincheng Power Supply Co of State Grid Shanxi Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
    • 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
    • H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/50—Arrangements for eliminating or reducing asymmetry in polyphase networks

Abstract

The invention relates to a three-phase unbalanced reactive power optimization method and a system thereof, belonging to the technical field of three-phase unbalanced reactive power optimization methods; the technical problem to be solved is as follows: the improvement of a three-phase unbalance reactive power optimization method is provided; the technical scheme for solving the technical problems is as follows: establishing a reactive power optimization model by using an improved genetic algorithm and taking the minimum of the network loss of the power grid and the negative sequence voltage of the system as a target function, combining a simple genetic algorithm with a nonlinear programming method, and solving the problem of a nonlinear minimum value by using an fmincon function; the method has the advantages that pheromone distribution used in the initial stage of the ant colony algorithm is generated by utilizing the rapid and random global search capability of the genetic algorithm, the global optimal solution is obtained by combining the outstanding local search capability of the ant colony algorithm according to the distribution condition of the initial pheromone, the accuracy and the convergence speed are both better superior, and effective reference and guidance basis are provided for reactive power optimization of a three-phase unbalanced power grid system; the method is applied to three-phase unbalanced reactive power optimization.

Description

Three-phase unbalanced reactive power optimization method and system thereof
Technical Field
The invention discloses a three-phase unbalanced reactive power optimization method and a system thereof, belonging to the technical field of three-phase unbalanced reactive power optimization methods and systems thereof.
Background
With the development of technology and the popularization of electric power, the reactive power optimization of the power distribution network presents a multivariable multi-constraint highly nonlinear optimization problem, wherein the complexity of the reactive power optimization of the power distribution network is aggravated by the occurrence of three-phase imbalance. In the prior art, the reactive power optimization of the power distribution network uses a traditional mathematical method to perform reactive power optimization, but the traditional optimization method has great dependence on the accuracy of an optimization model and is difficult to meet the requirement of real-time control.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to solve the technical problems that: an improvement of a three-phase imbalance reactive power optimization method is provided.
In order to solve the technical problems, the invention adopts the technical scheme that: a three-phase unbalance reactive power optimization method comprises the following steps:
the method comprises the following steps: acquiring a data set of a power network system, wherein the data set comprises power network parameters, control variables, state variables and constraint conditions;
step two: constructing a target function of system evaluation, wherein the target function is established by taking the minimum value of the negative sequence voltage of the power distribution network system and the loss of the power distribution network as a target;
step three: selecting analysis constraint conditions, wherein the analysis constraint conditions are used for limiting the upper limit and the lower limit of each parameter in the power distribution network;
step four: and selecting an optimal combination mode according to a genetic algorithm.
The control variable in the first step comprises an adjustable transformer tap position TKiReactive compensation capacity QCiGenerator terminal voltage UGi；
The state variable comprises a load node voltage U of the generatorDiReactive output QGiBranch reactive power qb；
The constraint conditions comprise active power injected into the node i, reactive power injected into the node i and three-phase unbalance rate K of the node iiThe number of groups of each item in the compensation capacitor, the gear T of the on-load tap changing transformer, the three-phase switching times in the compensation capacitor and the gear adjusting times of the tap joint of the on-load tap changing transformer.
The calculation formula of the objective function in the second step is as follows:
Figure BDA0002785435040000011
in the above formula: lambda [ alpha ]1Weight, lambda, representing the negative sequence voltage of a distribution network system2Weight, U, representing network loss of the distribution networknegIndicating negative sequence voltage, P, of the distribution network systemlossNetwork loss of the distribution network representing mutual impedance between three phases,
Figure BDA0002785435040000012
The expression objective function only considers the negative sequence voltage of the distribution network system,
Figure BDA0002785435040000021
representing that the objective function only considers network loss;
the U isnegThe calculation formula of (2) is as follows:
Figure BDA0002785435040000022
in the above formula: n is a radical ofERepresenting the set of all nodes in the distribution network system, ei，-Representing the real part of the negative sequence voltage of node i, fi，-Represents the imaginary part of the negative sequence voltage of the node i;
the calculation formula of the negative sequence voltage of the node i is as follows:
Figure BDA0002785435040000023
α＝1∠120°；
in the above formula:
Figure BDA0002785435040000024
representing the negative sequence voltage of the node i,
Figure BDA0002785435040000025
the a-phase voltage at node i is represented,
Figure BDA0002785435040000026
the b-phase voltage at node i is represented,
Figure BDA0002785435040000027
represents the c-phase voltage of the node i;
the P islossThe calculation formula of (2) is as follows:
Figure BDA0002785435040000028
in the above formula: beta represents three phases of a, b and c,
Figure BDA0002785435040000029
representing the real parts of the elements corresponding to the three phases of node i and the three phases of node j in the node admittance matrix,
Figure BDA00027854350400000210
representing the imaginary parts of the elements corresponding to the three phases of the node i and the three phases of the node j in the node admittance matrix, gamma represents the three phases a, b and c, and N1Representing a set of all branches of the distribution network;
Figure BDA00027854350400000211
represents the real part of the gamma phase voltage at node i,
Figure BDA00027854350400000212
representing the imaginary part of the gamma phase voltage at node i.
The constraint conditions in the third step include equality constraint conditions and inequality constraint conditions, the equality constraint conditions include active balance constraint conditions and reactive balance constraint conditions of the nodes, and the calculation formula of the active balance constraint conditions is as follows:
Figure BDA00027854350400000213
in the above formula: pGiRepresenting the active power, P, generated by the generatorLiIndicating the active power, U, required by the useriRepresenting the voltage at node i, UjRepresenting the voltage of node j, GijRepresenting susceptances of branches i to j, BijRepresenting the conductance, theta, of branches i to jijRepresenting the phase angle difference of nodes i and j;
the calculation formula of the reactive balance constraint condition is as follows:
Figure BDA00027854350400000214
in the above formula: qGiRepresenting reactive power, Q, generated by the generatorLiRepresenting reactive power consumed by the user, QCiRepresenting the reactive power of the compensation; u shapeiRepresenting the voltage at node i, UjRepresenting the voltage of node j, GijRepresenting susceptances of branches i to j, BijRepresenting the conductance, theta, of branches i to jijRepresenting nodes i and jPhase angle difference;
the inequality constraint conditions comprise control variable constraint conditions, state variable constraint conditions and configuration constraint conditions, and the calculation formula of the control variable constraint conditions is as follows:
Figure BDA0002785435040000031
in the above formula: t isKiIndicating the position of the tap of the adjustable transformer, QCiRepresenting reactive compensation capacity, UGiRepresenting generator terminal voltage, UGimaxRepresenting the maximum value of the terminal voltage of the generator, UGiminRepresenting the minimum value of the terminal voltage of the generator, TKiminIndicating the minimum value of the tap position of the adjustable transformer, TKimaxIndicating the maximum value of the position of the tap of the adjustable transformer, QCiminRepresenting the minimum value of the reactive compensation capacity, QCimaxRepresents the maximum value of the reactive compensation capacity;
the calculation formula of the state variable constraint condition is as follows:
Figure BDA0002785435040000032
in the above formula: u shapeDiRepresenting the load node voltage, Q, of the generatorGiRepresenting reactive power, qbRepresenting branch reactive power;
the calculation formula of the configuration constraint condition is as follows:
Figure BDA0002785435040000033
Figure BDA0002785435040000034
in the above formula: kiRepresents the three-phase imbalance ratio, K, of the node iimaxRepresents the maximum three-phase unbalance rate of the allowed node i, and T represents the on-load tap changerGear position of the pressure transformer, TminRepresenting the minimum value of the on-load tap changer gear, TmaxRepresenting the maximum value of the on-load tap changer gear, NTIndicating the number of on-load transformer tap-position adjustments, NTmaxIndicating the maximum allowable number of adjustments of the on-load transformer tap, CmmaxThe maximum number of compensation capacitor sets is indicated,
Figure BDA0002785435040000035
represents the number of groups of a-phases in the compensation capacitor m,
Figure BDA0002785435040000036
represents the number of groups of b phases in the compensation capacitor m,
Figure BDA0002785435040000037
representing the number of groups of c-phases, N, in the compensation capacitor mcmmsxRepresents the maximum switching times of the compensation capacitor m,
Figure BDA0002785435040000038
represents the switching times of the a phase in the compensation capacitor m,
Figure BDA0002785435040000039
represents the switching times of the b phase in the compensation capacitor m,
Figure BDA00027854350400000310
representing the switching times of the c phase in the compensation capacitor m;
and a nonlinear programming method is adopted in the calculation process of the constraint conditions, the data is processed by utilizing an Fmincon function, predefined upper and lower limits of the processed data are given, and an extreme value is obtained.
The specific steps of selecting the optimal combination mode according to the genetic algorithm in the fourth step are as follows:
step 4.1: initializing population data and randomly generating a parent;
step 4.2: evaluating the individual fitness according to the fitness function;
step 4.3: selecting a father string chromosome to carry out evolution operation;
step 4.4: judging whether evolution rule meeting conditions are met, generating a plurality of optimal solutions when the evolution rule meeting conditions are met, and returning to the step 4.2 with a generated result when the evolution rule meeting conditions are not met;
step 4.5: converting the generated optimized solution into initial pheromone distribution;
step 4.6: randomly distributing a plurality of ants on the nodes for searching;
step 4.7: calculating fitness and updating pheromones on a new path;
step 4.8: and outputting the ant colony optimal solution and judging whether the circulation meets the termination condition, outputting the global optimal solution when the condition is met, and returning to the step 4.6 when the condition is not met.
Selecting a father string chromosome in the step 4.3 to carry out evolution operation, wherein the evolution operation specifically comprises selection, crossing and mutation operation, the selection operation adopts a roulette mode to carry out operation, and the probability of the selected individual is in direct proportion to the fitness of the individual;
the cross operation adopts self-adaptive cross operation, when the fitness areas of all the populations are consistent, the cross probability is increased, when the population fitness is dispersed, the cross probability is reduced, and the cross probability is PcSaid P iscThe calculation formula of (2) is as follows:
Figure BDA0002785435040000041
in the above formula: f is fmaxRepresenting the maximum fitness value in the population, faverageDenotes the mean fitness value of the population, f denotes the greater fitness value of the two crossed individuals, k1Represents a constant, k2Represents a constant;
the mutation operation adopts self-adaptive mutation operation, when the individual fitness areas of the population are consistent, the mutation probability is increased, when the population fitness is dispersed, the mutation probability is reduced, and the mutation probability is PmSaid P ismThe calculation formula of (2) is as follows:
Figure BDA0002785435040000042
in the above formula: f' denotes the fitness value of the variant individual, k3Represents a constant, k4Represents a constant;
for an individual with a fitness value higher than the population average fitness value, it corresponds to a low cross probability and a low mutation probability, so that the individual is protected; for individuals with fitness values below the mean fitness value, which corresponds to high crossover probability and high variation probability, the individual is eliminated.
The pheromone on the new path updated in the step 4.7 is further to obtain an initial optimization solution through a genetic algorithm, set the distribution of the initial pheromone according to the obtained initial optimization solution and adjust the initial pheromone by adopting a forced means;
the updating formula of the pheromone is as follows:
Figure BDA0002785435040000051
Figure BDA0002785435040000052
in the above formula: sigma represents a penalty factor for reducing pheromone concentration, rho represents the volatilization rate of pheromones, and lkRepresenting paths through two cities of ij, Q representing intensity of pheromone, LkRepresenting the path length that would have been traversed during the iteration.
The system comprises a first module for acquiring a network system data set, the first module feeds acquired system data back to a data processing center through the Internet to perform data cooperation and constraint limitation, and the data processing center comprises a second module for establishing an objective function, a third module for limiting constraint conditions and a fourth module for selecting an optimal combination mode.
The first module further comprises an information acquisition module and an information feedback module; the information acquisition module acquires data through power equipment deployed in a power grid, and the information feedback module is used for feeding back the acquired data to the data processing center through the Internet to limit cooperation and constraint among the data.
Compared with the prior art, the invention has the beneficial effects that: according to the method, through an improved genetic algorithm, a reactive power optimization model is established by taking the minimum of the network loss of the power grid and the negative sequence voltage of the system as a target function, a simple genetic algorithm is combined with a nonlinear programming method, the problem of a nonlinear minimum value is solved through an fmincon function, and the nonlinear minimum value is used for limiting the numerical value of a constraint condition, so that the optimization performance is improved; the method further utilizes the rapid and random global search capability of the genetic algorithm to generate the pheromone distribution used in the initial stage of the ant colony algorithm, obtains the global optimal solution by combining the outstanding local search capability of the ant colony algorithm according to the distribution condition of the initial pheromone, has better superiority in both precision and convergence rate, and provides effective reference and guidance basis for the reactive power optimization of the three-phase unbalanced power grid system.
Drawings
The invention is further described below with reference to the accompanying drawings:
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a flow chart of the improved genetic algorithm of the present invention.
Detailed Description
As shown in fig. 1 and fig. 2, the three-phase imbalance reactive power optimization method of the present invention includes the following specific implementation steps:
the method comprises the steps of firstly, acquiring a data set of the power network system, and using the data set as source data applied to genetic algorithm evolution to obtain a combined recommendation scheme. And acquiring real-time parameter data through the equipment related to the power distribution network. In the step, the data set acquisition is divided into power network parameters, control variables, state variables and constraint conditions. Wherein the control variable comprises an adjustable transformer tap position TKiReactive compensation capacity QCiGenerator terminal voltage UGi(ii) a The state variable comprises a load node voltage U of the generatorDiReactive output QGiBranch reactive power qb(ii) a The constraints include the active power injected into node i,Reactive power injected into node i and three-phase unbalance rate K of node iiThe number of groups of each item in the compensation capacitor, the gear T of the on-load tap changing transformer, the three-phase switching times in the compensation capacitor and the gear adjusting times of the tap joint of the on-load tap changing transformer.
And step two, constructing a system evaluation objective function for being used as the evaluation of the beneficial effect obtained by aiming at the data combination in the power distribution network. Further, the method aims at the minimum of the system negative sequence voltage of the power distribution network and the loss of the power grid network, and establishes an objective function as follows:
Figure BDA0002785435040000061
wherein, UnegIndicating negative sequence voltage, P, of the distribution network systemlossNetwork loss of the distribution network representing mutual impedance between three phases,
Figure BDA0002785435040000062
The expression objective function only considers the negative sequence voltage of the distribution network system,
Figure BDA0002785435040000063
representing an objective function that only takes into account network loss, λ1Weight, lambda, representing the negative sequence voltage of a distribution network system2A weight representing the network loss of the distribution network.
Figure BDA0002785435040000064
Wherein, UnegIndicating negative sequence voltage, N, of the distribution network systemERepresenting the set of all nodes in the distribution network system, ei，-Representing the real part of the negative sequence voltage of node i, fi，-Representing the imaginary part of the negative sequence voltage at node i.
Figure BDA0002785435040000065
Wherein, PlossThe loss of the network of the distribution network of the mutual impedance among three phases is shown, beta represents three phases of a, b and c,
Figure BDA0002785435040000066
representing the real parts of the elements corresponding to the three phases of node i and the three phases of node j in the node admittance matrix,
Figure BDA0002785435040000067
representing the imaginary parts of the elements corresponding to the three phases of the node i and the three phases of the node j in the node admittance matrix, gamma represents the three phases a, b and c, and N1Representing a set of all branches of the distribution network;
Figure BDA0002785435040000068
represents the real part of the gamma phase voltage at node i,
Figure BDA0002785435040000069
the imaginary part representing the gamma phase voltage at node i;
Figure BDA00027854350400000610
α＝1∠120°
wherein the content of the first and second substances,
Figure BDA00027854350400000611
representing the negative sequence voltage of the node i,
Figure BDA00027854350400000612
the a-phase voltage at node i is represented,
Figure BDA00027854350400000613
the b-phase voltage at node i is represented,
Figure BDA00027854350400000614
representing the c-phase voltage at node i.
And step three, selecting an analysis constraint condition, wherein the constraint condition is used for limiting the upper limit and the lower limit of the number of the individual parameters in the power distribution network. Aiming at the established optimization model. The constraint conditions are further divided into equality constraints and inequality constraints, wherein the equality constraints are further active balance constraints and reactive balance constraints of the nodes, and the active balance constraints are further expressed as:
Figure BDA00027854350400000615
wherein, PGiRepresenting the active power, P, generated by the generatorLiIndicating the active power, U, required by the useriRepresenting the voltage at node i, UjRepresenting the voltage of node j, GijRepresenting susceptances of branches i to j, BijRepresenting the conductance, theta, of branches i to jijRepresenting the phase angle difference of nodes i and j.
The reactive power balance is as follows:
Figure BDA0002785435040000071
wherein Q isGiRepresenting reactive power, Q, generated by the generatorLiRepresenting reactive power consumed by the user, QCiRepresenting the reactive power of the compensation; u shapeiRepresenting the voltage at node i, UjRepresenting the voltage of node j, GijRepresenting susceptances of branches i to j, BijRepresenting the conductance, theta, of branches i to jijRepresenting the phase angle difference of nodes i and j;
the inequality constraint conditions are further control variable constraints, state variable constraints and configuration constraints, and the control variable constraints are
Figure BDA0002785435040000072
Wherein, TKiIndicating the tap position, Q, of an adjustable transformerCiRepresenting reactive compensation capacity, UGiRepresenting generator terminal voltage, UGimaxRepresenting the maximum value of the terminal voltage of the generator, UGiminA minimum value of the electric machine terminal voltage; the state variableThe constraints are:
Figure BDA0002785435040000073
wherein, UDiRepresenting the load node voltage, Q, of the generatorGiRepresenting reactive power, qbRepresenting branch reactive power; the configuration constraints are:
Figure BDA0002785435040000074
Figure BDA0002785435040000075
wherein, KiRepresents the three-phase imbalance ratio, K, of the node iimaxRepresents the maximum three-phase unbalance rate of an allowable node i, T represents the gear of the on-load tap changing transformer, TminRepresenting the minimum value of the on-load tap changer gear, TmaxRepresenting the maximum value of the on-load tap changer gear, NTIndicating the number of on-load transformer tap-position adjustments, NTmaxIndicating the maximum allowable number of adjustments of the on-load transformer tap, CmmaxThe maximum number of compensation capacitor sets is indicated,
Figure BDA0002785435040000076
represents the number of groups of a-phases in the compensation capacitor m,
Figure BDA0002785435040000077
represents the number of groups of b phases in the compensation capacitor m,
Figure BDA0002785435040000078
representing the number of groups of c-phases, N, in the compensation capacitor mcmmsxRepresents the maximum switching times of the compensation capacitor m,
Figure BDA0002785435040000079
represents the switching times of the a phase in the compensation capacitor m,
Figure BDA00027854350400000710
represents the switching times of the b phase in the compensation capacitor m,
Figure BDA00027854350400000711
representing the switching times of the c phase in the compensation capacitor m;
and in the calculation process, a nonlinear programming method is adopted, the data is processed by utilizing the Fmincon function, predefined upper and lower limits are given to the processed data, and an extreme value is obtained.
And step four, selecting an optimal combination mode according to a genetic algorithm. The steps are further divided as follows:
and 4.1, initializing population data, randomly generating a parent, adopting real number coding for a coding mode, and randomly generating individual initialization within a search boundary.
And 4.2, evaluating the individual fitness according to the fitness function, finishing the mapping relation between the objective function reflecting the problem to be optimized and the algorithm evolution search direction, and considering the size of the objective function value of the problem to be optimized and the satisfaction degree of the individual to the condition with the constraint condition in the optimization evolution.
And 4.3, selecting the father string chromosomes to carry out evolution operation, wherein the evolution operation further comprises selection, crossing and mutation operation.
And 4.4, judging whether the evolution compliance condition is met, if so, generating a plurality of optimal solutions, and if not, carrying the generated result back to the step 4.2.
And 4.5, converting the generated optimized solution into initial pheromone distribution.
And 4.6, randomly distributing a plurality of ants on the nodes for searching.
And 4.7, calculating the fitness and updating the pheromone on the new path.
And 4.8, outputting the ant colony optimal solution and judging whether the loop meets the termination condition, if so, outputting the global optimal solution, and if not, returning to the step 4.6.
Wherein the step 4.3 of selecting in the evolution operationThe selection operation is performed in a roulette mode, specifically, the probability of the selected individual is in direct proportion to the fitness of the individual, and the individual with the excellent fitness is stored and enters the next generation; and the cross operation is self-adaptive cross operation, and the variant individuals and a certain target individual are subjected to cross mixing according to the selection probability. When the individual fitness areas of the population are consistent, the cross probability is increased; when the population fitness is dispersed, the cross probability is reduced, and the cross probability is Pc：
Figure BDA0002785435040000081
Wherein f ismaxRepresenting the maximum fitness value in the population, faverageDenotes the mean fitness value of the population, f denotes the greater fitness value of the two crossed individuals, k1Represents a constant, k2Represents a constant;
the variation operation is self-adaptive variation operation, the difference vector of two individuals randomly selected from the population is used as a random variation source of a third individual, and the weighted difference vector is blended into the third individual according to a preset rule to generate a variation individual. When the individual fitness areas of the population are consistent, the variation probability is increased; when the population fitness is dispersed, the mutation probability is reduced, and the mutation probability is Pm：
Figure BDA0002785435040000091
Wherein f' represents the fitness value of the variant individual, k3Represents a constant, k4Represents a constant; for an individual with a fitness value higher than the population average fitness value, it corresponds to a low cross probability and a low mutation probability, so that the individual is protected; for individuals with fitness values below the mean fitness value, this corresponds to a high probability of crossover and a high probability of variation, and the individuals are eliminated.
Wherein, the pheromone on the new path updated in the step 4.7 is further to obtain an initial optimization solution through a genetic algorithm, set the distribution of the initial pheromone according to the obtained initial optimization solution and adjust the initial pheromone by adopting a forced means; wherein the update of the pheromone is represented as:
Figure BDA0002785435040000092
Figure BDA0002785435040000093
where σ denotes a penalty factor for reducing the pheromone concentration, ρ denotes the pheromone volatility, lkRepresenting a path through two cities of ii, Q representing the intensity of the pheromone, LkRepresenting the path length that would have been traversed during the iteration.
Based on the three-phase unbalanced reactive power optimization method, a three-phase unbalanced reactive power optimization system for realizing the method is further provided, and comprises the following modules:
a first module for obtaining a network system data set; the module further comprises an information acquisition module and an information feedback module; the information acquisition module acquires data through power equipment deployed in a power grid, and the information feedback module is used for feeding back the acquired data to a data processing center through the Internet to perform data cooperation and constraint limitation; the data acquisition comprises a data set comprising power network parameters, control variables, state variables and constraint conditions; the control variable comprises an adjustable transformer tap position TKiReactive compensation capacity QCiGenerator terminal voltage UGi(ii) a The state variable comprises a load node voltage U of the generatorDiReactive output QGiBranch reactive power qb(ii) a The constraint conditions comprise active power injected into the node i, reactive power injected into the node i and three-phase unbalance rate K of the node iiThe number of groups of each item in the compensation capacitor, the number of three-phase switching times in the tap position T of the voltage regulating transformer, the number of three-phase switching times in the compensation capacitor and the number of on-load transformer tap position adjustment times.
A second module for establishing an objective function; the module further establishes a target function by taking the minimum of system negative sequence voltage and grid network loss in the power distribution network as a target; a third module for defining constraints; the module is a fourth module, wherein the constraint conditions comprise equality constraint and inequality constraint, the equality constraint further comprises active balance constraint and reactive balance constraint of the nodes, and the fourth module is used for selecting an optimal combination mode; the module further initializes population data and randomly generates a parent; secondly, evaluating the individual fitness according to a fitness function, and selecting a father-string chromosome to carry out evolution operation; thirdly, judging whether evolution rule meeting conditions are met or not according to the obtained results, if yes, generating a plurality of optimal solutions, and if not, carrying the generated results and returning to fitness evaluation for circulation; secondly, converting the generated optimization solution into initial pheromone distribution, and randomly distributing a plurality of ants on the nodes for searching; and finally, calculating the fitness and updating the pheromone on the new path, outputting an ant colony optimal solution, further judging whether the loop meets a termination condition, if so, outputting a global optimal solution, and if not, returning to the random ant distribution and then iterating.
It should be noted that, regarding the specific structure of the present invention, the connection relationship between the modules adopted in the present invention is determined and can be realized, except for the specific description in the embodiment, the specific connection relationship can bring the corresponding technical effect, and the technical problem proposed by the present invention is solved on the premise of not depending on the execution of the corresponding software program.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (9)

1. A three-phase unbalance reactive power optimization method is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: acquiring a data set of a power network system, wherein the data set comprises power network parameters, control variables, state variables and constraint conditions;
step two: constructing a target function of system evaluation, wherein the target function is established by taking the smaller value of the negative sequence voltage of the power distribution network system and the network loss of the power distribution network as a target;
step three: selecting analysis constraint conditions, wherein the analysis constraint conditions are used for limiting the upper limit and the lower limit of each parameter in the power distribution network;
step four: and selecting an optimal combination mode according to a genetic algorithm.
2. The three-phase imbalance reactive power optimization method according to claim 1, wherein: the control variable in the first step comprises an adjustable transformer tap position TKiReactive compensation capacity QCiGenerator terminal voltage UGi；
The state variable comprises a load node voltage U of the generatorDiReactive output QGiBranch reactive power qb；
The constraint conditions comprise active power injected into the node i, reactive power injected into the node i and three-phase unbalance rate K of the node iiThe number of groups of each item in the compensation capacitor, the gear T of the on-load tap changing transformer, the three-phase switching times in the compensation capacitor and the gear adjusting times of the tap joint of the on-load tap changing transformer.
3. A method of reactive power optimization for three-phase imbalance according to claim 2, wherein: the calculation formula of the objective function in the second step is as follows:
Figure FDA0002785435030000011
in the above formula: lambda [ alpha ]1Weight, lambda, representing the negative sequence voltage of a distribution network system2Weight, U, representing network loss of the distribution networknegIndicating negative sequence voltage, P, of the distribution network systemlossNetwork loss of the distribution network representing mutual impedance between three phases,
Figure FDA0002785435030000012
The expression objective function only considers the negative sequence voltage of the distribution network system,
Figure FDA0002785435030000013
representing that the objective function only considers network loss;
the U isnegThe calculation formula of (2) is as follows:
Figure FDA0002785435030000014
in the above formula: n is a radical ofERepresenting the set of all nodes in the distribution network system, ei，-Representing the real part of the negative sequence voltage of node i, fi，-Represents the imaginary part of the negative sequence voltage of the node i;
the calculation formula of the negative sequence voltage of the node i is as follows:
Figure FDA0002785435030000015
α＝1∠120°；
in the above formula:
Figure FDA0002785435030000016
-represents the negative sequence voltage of node i,
Figure FDA0002785435030000017
the a-phase voltage at node i is represented,
Figure FDA0002785435030000018
the b-phase voltage at node i is represented,
Figure FDA0002785435030000019
represents the c-phase voltage of the node i;
the P islossThe calculation formula of (2) is as follows:
Figure FDA0002785435030000021
in the above formula: beta represents three phases of a, b and c,
Figure FDA0002785435030000022
representing the real parts of the elements corresponding to the three phases of node i and the three phases of node j in the node admittance matrix,
Figure FDA0002785435030000023
representing the imaginary parts of the elements corresponding to the three phases of the node i and the three phases of the node j in the node admittance matrix, gamma represents the three phases a, b and c, and N1Representing the set of all the branches of the distribution network,
Figure FDA0002785435030000024
represents the real part of the gamma phase voltage at node i,
Figure FDA0002785435030000025
representing the imaginary part of the gamma phase voltage at node i.
4. A method of reactive power optimization for three-phase imbalance according to claim 3, wherein: the constraint conditions in the third step include equality constraint conditions and inequality constraint conditions, the equality constraint conditions include active balance constraint conditions and reactive balance constraint conditions of the nodes, and the calculation formula of the active balance constraint conditions is as follows:
Figure FDA0002785435030000026
in the above formula: pGiRepresenting the active power, P, generated by the generatorLiIndicating the active power, U, required by the useriRepresenting the voltage at node i, UjRepresenting the voltage of node j, GijRepresenting susceptances of branches i to j, BijRepresenting the conductance, theta, of branches i to jijRepresenting the phase angle difference of nodes i and j;
the calculation formula of the reactive balance constraint condition is as follows:
Figure FDA0002785435030000027
in the above formula: qGiRepresenting reactive power, Q, generated by the generatorLiRepresenting reactive power consumed by the user, QCiRepresenting the reactive power of the compensation; u shapeiRepresenting the voltage at node i, UjRepresenting the voltage of node j, GijRepresenting susceptances of branches i to j, BijRepresenting the conductance, theta, of branches i to jijRepresenting the phase angle difference of nodes i and j;
the inequality constraint conditions comprise control variable constraint conditions, state variable constraint conditions and configuration constraint conditions, and the calculation formula of the control variable constraint conditions is as follows:
Figure FDA0002785435030000028
in the above formula: t isKiIndicating the position of the tap of the adjustable transformer, QCiRepresenting reactive compensation capacity, UGiRepresenting generator terminal voltage, UGimaxIndicating generation of electricityMaximum value of terminal voltage of electric machine, UGiminRepresenting the minimum value of the terminal voltage of the generator, TKiminIndicating the minimum value of the tap position of the adjustable transformer, TKimaxIndicating the maximum value of the position of the tap of the adjustable transformer, QCiminRepresenting the minimum value of the reactive compensation capacity, QCimaxRepresents the maximum value of the reactive compensation capacity;
the calculation formula of the state variable constraint condition is as follows:
Figure FDA0002785435030000031
in the above formula: u shapeDiRepresenting the load node voltage, Q, of the generatorGiRepresenting reactive power, qbRepresenting branch reactive power;
the calculation formula of the configuration constraint condition is as follows:
Figure FDA0002785435030000032
Figure FDA0002785435030000033
in the above formula: kiRepresents the three-phase imbalance ratio, K, of the node iimaxRepresents the maximum three-phase unbalance rate of an allowable node i, T represents the gear of the on-load tap changing transformer, TminRepresenting the minimum value of the on-load tap changer gear, TmaxRepresenting the maximum value of the on-load tap changer gear, NTIndicating the number of on-load transformer tap-position adjustments, NTmaxIndicating the maximum allowable number of adjustments of the on-load transformer tap, CmmaxThe maximum number of compensation capacitor sets is indicated,
Figure FDA0002785435030000034
represents the number of groups of a-phases in the compensation capacitor m,
Figure FDA0002785435030000035
represents the number of groups of b phases in the compensation capacitor m,
Figure FDA0002785435030000036
representing the number of groups of c-phases, N, in the compensation capacitor mcmmsxRepresents the maximum switching times of the compensation capacitor m,
Figure FDA0002785435030000037
represents the switching times of the a phase in the compensation capacitor m,
Figure FDA0002785435030000038
represents the switching times of the b phase in the compensation capacitor m,
Figure FDA0002785435030000039
representing the switching times of the c phase in the compensation capacitor m;
and a nonlinear programming method is adopted in the calculation process of the constraint conditions, the data is processed by utilizing an Fmincon function, predefined upper and lower limits of the processed data are given, and an extreme value is obtained.
5. The method according to claim 4, wherein the method comprises the following steps: the specific steps of selecting the optimal combination mode according to the genetic algorithm in the fourth step are as follows:
step 4.1: initializing population data and randomly generating a parent;
step 4.2: evaluating the individual fitness according to the fitness function;
step 4.3: selecting a father string chromosome to carry out evolution operation;
step 4.4: judging whether evolution rule meeting conditions are met, generating a plurality of optimal solutions when the evolution rule meeting conditions are met, and returning to the step 4.2 with a generated result when the evolution rule meeting conditions are not met;
step 4.5: converting the generated optimized solution into initial pheromone distribution;
step 4.6: randomly distributing a plurality of ants on the nodes for searching;
step 4.7: calculating fitness and updating pheromones on a new path;
step 4.8: and outputting the ant colony optimal solution and judging whether the circulation meets the termination condition, outputting the global optimal solution when the condition is met, and returning to the step 4.6 when the condition is not met.
6. The method according to claim 5, wherein the method comprises the following steps: selecting a father string chromosome in the step 4.3 to carry out evolution operation, wherein the evolution operation specifically comprises selection, crossing and mutation operation, the selection operation adopts a roulette mode to carry out operation, and the probability of the selected individual is in direct proportion to the fitness of the individual;
the cross operation adopts self-adaptive cross operation, when the fitness areas of all the populations are consistent, the cross probability is increased, when the population fitness is dispersed, the cross probability is reduced, and the cross probability is PcSaid P iscThe calculation formula of (2) is as follows:
Figure FDA0002785435030000041
in the above formula: f is fmaxRepresenting the maximum fitness value in the population, faverageDenotes the mean fitness value of the population, f denotes the greater fitness value of the two crossed individuals, k1Represents a constant, k2Represents a constant;
the mutation operation adopts self-adaptive mutation operation, when the individual fitness areas of the population are consistent, the mutation probability is increased, when the population fitness is dispersed, the mutation probability is reduced, and the mutation probability is PmSaidPThe formula for m is:
Figure FDA0002785435030000042
in the above formula: f' denotes the fitness value of the variant individual, k3Represents a constant, k4Represents a constant；
For an individual with a fitness value higher than the population average fitness value, it corresponds to a low cross probability and a low mutation probability, so that the individual is protected; for individuals with fitness values below the mean fitness value, which corresponds to high crossover probability and high variation probability, the individual is eliminated.
7. The method according to claim 5, wherein the method comprises the following steps: the pheromone on the new path updated in the step 4.7 is further to obtain an initial optimization solution through a genetic algorithm, set the distribution of the initial pheromone according to the obtained initial optimization solution and adjust the initial pheromone by adopting a forced means;
the updating formula of the pheromone is as follows:
Figure FDA0002785435030000043
Figure FDA0002785435030000044
in the above formula: sigma represents a penalty factor for reducing pheromone concentration, rho represents the volatilization rate of pheromones, and lkRepresenting paths through two cities of ij, Q representing intensity of pheromone, LkRepresenting the path length that would have been traversed during the iteration.
8. The utility model provides an unbalanced three phase reactive power optimization system which characterized in that: the system comprises a first module for acquiring a network system data set, the first module feeds acquired system data back to a data processing center through the Internet to perform data cooperation and constraint limitation, and the data processing center comprises a second module for establishing an objective function, a third module for limiting constraint conditions and a fourth module for selecting an optimal combination mode.
9. The reactive power optimization system of claim 8, wherein the reactive power optimization system further comprises: the first module further comprises an information acquisition module and an information feedback module; the information acquisition module acquires data through power equipment deployed in a power grid, and the information feedback module is used for feeding back the acquired data to the data processing center through the Internet to limit cooperation and constraint among the data.
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