CN115276022A - Multi-objective power flow voltage optimization method considering distributed photovoltaic access for power distribution network - Google Patents

Multi-objective power flow voltage optimization method considering distributed photovoltaic access for power distribution network Download PDF

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CN115276022A
CN115276022A CN202211052652.0A CN202211052652A CN115276022A CN 115276022 A CN115276022 A CN 115276022A CN 202211052652 A CN202211052652 A CN 202211052652A CN 115276022 A CN115276022 A CN 115276022A
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power
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distribution network
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张华赢
李艳
史帅彬
汪清
朱明星
刘威
孙贺
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Shenzhen Power Supply Bureau 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

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Abstract

The invention relates to a multi-target power flow voltage optimization method for a power distribution network considering distributed photovoltaic access, wherein a disturbance mechanism of a balance optimizer algorithm is added on the basis of a multi-target grey wolf colony algorithm search mechanism, so that the search range of a multi-target colony intelligent algorithm is further expanded, and the convergence and the distribution of algorithm search results are effectively improved; compared with the existing group intelligent optimization algorithm, the optimization method provided by the invention can provide a high-quality and various flow voltage optimization schemes for decision makers, and can better meet the decision requirements of the decision makers under different target preferences, so that the scene requirement of the actual power distribution network flow voltage optimization can be better met.

Description

Multi-objective power flow voltage optimization method considering distributed photovoltaic access for power distribution network
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a multi-target power flow voltage optimization method for a power distribution network considering distributed photovoltaic access.
Background
A large amount of distributed photovoltaic power is connected to a power distribution network, so that a series of electric energy quality problems such as power grid loss increase, power flow fluctuation, high terminal voltage, head end power flow reverse transmission, three-phase imbalance and the like can be caused, and brand new challenges are brought to safe, stable and economic operation of a power distribution system. Therefore, multi-objective tidal current voltage optimization of the power distribution network considering the distributed photovoltaic access scene is an urgent need for guaranteeing the power quality and safe and stable operation of the power distribution network.
In practical application, an intelligent optimization algorithm combining a multi-objective Pareto domination theory is generally adopted to perform multi-objective power flow voltage optimization on a power distribution network, and the main optimization algorithms include a multi-objective particle swarm optimization (MOPSO), a genetic algorithm (NSGA-II) based on non-dominant ranking, a multi-objective evolutionary algorithm (MOEA), a multi-objective grey wolf colony algorithm (MOGWOO), a multi-objective balance optimizer algorithm (MOEO) and the like.
In an application scenario of multi-target tidal current and voltage optimization of a power distribution network, due to limitations of a group updating method and an external archive maintenance mechanism, a solution result obtained by an existing group intelligent optimization algorithm generally has the problems of low convergence speed, easiness in falling into local optimization and poor Pareto front edge distribution performance.
Disclosure of Invention
The invention aims to provide a power distribution network multi-target tidal current voltage optimization method considering distributed photovoltaic access, and aims to solve the technical problems that a solving result obtained by the existing group intelligent optimization algorithm is low in convergence speed, easy to fall into local optimization and poor in Pareto front edge distribution performance.
In order to achieve the purpose, the invention provides a multi-target power flow voltage optimization method for a power distribution network considering distributed photovoltaic access, which comprises the following steps:
s1, collecting and processing operation data of each monitoring point in a power distribution network and nameplate parameters of each power device;
s2, performing system modeling on the power distribution network according to the operation data and the nameplate parameters, and establishing a multi-target power flow voltage optimization control model;
s3, setting initial parameters of a power flow voltage optimization algorithm, performing group initialization to obtain an initial group, calculating a target function value of each individual in the initial group, and judging whether the target function value meets constraint conditions or not; selecting individuals which are not dominated by other individuals from the initial population according to a multi-target Pareto domination principle and storing the individuals in an external file;
s4, selecting elite individuals from the external files, and performing population updating according to a preset population updating algorithm to obtain a new generation of population; calculating the objective function value of each individual in the new generation group, and judging whether the objective function value meets the constraint condition;
s5, screening out individuals which are not dominated by other individuals from the new generation group according to a multi-Pareto domination principle, storing the individuals into the external file, and updating the external file; and ensuring that the number of the individuals of the external files does not exceed a preset maximum value all the time through a diversity maintenance mechanism;
and S6, judging whether a preset iteration termination condition is met, if the preset iteration termination condition is not met, turning to the step S3 to continue the iteration, and if the preset iteration termination condition is met, stopping the iteration and obtaining a final power flow voltage optimization control scheme from the external file.
Preferably, the operation data comprises voltage, current and power of each monitoring point in different periods; each power device comprises a distributed photovoltaic device, a power transformer, a power line and a reactive power compensation device.
Preferably, the step S2 includes:
the method comprises the steps of taking minimum system loss, minimum average voltage deviation and minimum light rejection rate of distributed photovoltaic equipment as optimization targets, taking taps of on-load tap changers in all periods, compensation capacity of reactive compensation devices and active and reactive power output of the distributed photovoltaic equipment as decision variables, taking power flow balance constraint, system voltage operation constraint and element current operation constraint as constraint conditions, and establishing a power distribution network multi-target power flow voltage optimization model considering a distributed photovoltaic access scene.
Preferably, the step S3 includes:
based on a Monte Carlo method, randomly generating initial groups with a certain number of individuals in a range of decision variable upper and lower limit constraints, wherein each individual in the initial groups corresponds to a tidal current voltage control strategy;
after the initial group is generated, power flow and voltage distribution under each power flow voltage control strategy are obtained through power flow calculation, and then corresponding objective function values are calculated, and whether the objective function values meet the preset requirements of safe and stable operation of the power system is judged;
and comparing all the individuals in the initial population according to a Pareto domination mechanism and the objective function values of the individuals, and storing all the individuals not dominated by other individuals in the initial population into the external archive according to a comparison result.
Preferably, the step S4 includes:
dividing a target space into grids consisting of a plurality of hypercubes by using a sorting method based on a self-adaptive grid, screening the hypercubes in the grids by using a roulette selection method, and screening an individual from the screened hypercubes randomly and possibly; wherein the fewer the number of individuals contained by the hypercube that are not subject to other individuals, the higher the probability that the hypercube is screened;
repeating the steps for 3 times, screening 3 individuals from the external files as elite individuals, and respectively naming the elite individuals as alpha individuals, beta individuals and delta individuals;
updating each individual in the population by using a preset population updating method, wherein the updating method is as follows:
Figure BDA0003824319780000031
Figure BDA0003824319780000032
Figure BDA0003824319780000033
Figure BDA0003824319780000034
Figure BDA0003824319780000035
Figure BDA0003824319780000036
Figure BDA0003824319780000037
wherein K is the current iteration number of the algorithm, and K is the maximum iteration number of the algorithm; x (k) and X (k + 1) are n-dimensional decision variables corresponding to any individual in the k generation population and the k +1 generation population respectively, and X α (k)、X β (k)、X δ (k) N-dimensional decision variables corresponding to the alpha individual, the beta individual and the delta individual screened after the kth time are respectively selected; c 1 、C 2 、C 3 N-dimensional random vectors corresponding to alpha individual, beta individual and delta individual, respectively, and a compliance interval [ -2,2]Uniform distribution of the components; a. The 1 、A 2 、A 3 N-dimensional random vectors corresponding to alpha individual, beta individual and delta individual respectively, and obedience interval [ -a, a [ -a]Uniformly distributed; a satisfies the relation
Figure BDA0003824319780000041
The value of which decreases as the number of iterations increases; lambda [ alpha ] α 、λ β 、λ δ N-dimensional random numbers respectively corresponding to alpha individual, beta individual and delta individualVector, uniform distribution of units; f α 、F β 、F δ N-dimensional random vectors corresponding to the alpha individual, the beta individual and the delta individual respectively satisfy the relationship
Figure BDA0003824319780000042
Figure BDA0003824319780000043
r is an n-dimensional random vector that is uniformly distributed in units,
Figure BDA0003824319780000044
is the product of the element group operations; g α 、G β 、G δ N-dimensional random vectors corresponding to the alpha individual, the beta individual and the delta individual respectively satisfy the relation shown in the formula (9);
Figure BDA0003824319780000045
Figure BDA0003824319780000046
GCP＝0.5r 1 ×(r 2 ≥0.5)×r
Figure BDA0003824319780000047
Figure BDA0003824319780000048
GCP＝0.5r 1 ×(r 2 ≥0.5)×r
Figure BDA0003824319780000049
Figure BDA00038243197800000410
GCP＝0.5r 1 ×(r 2 ≥0.5)×r
wherein r is 1 And r 2 Are random numbers that are uniformly distributed in units.
Preferably, the step S5 includes:
the updating principle of the external file is as follows: comparing each non-dominated individual in the current population with all individuals in an external archive according to a Pareto domination principle, deleting all individuals dominated in the external archive, and storing a new generation of individuals not dominated by all individuals in the external archive into the external archive; calculating the crowding distance of each individual in the external archive based on a crowding distance calculation mechanism, and adopting the diversity of the external archive to carry out a maintenance mechanism to prevent the data capacity of the external archive from overflowing; wherein the congestion distance calculation mechanism is as follows: all the individuals in the external files are sorted according to the size of the objective function value, the individual with the minimum function value or the maximum function value is regarded as an extreme individual, and the other individuals are regarded as intermediate individuals; setting the crowding distance of the extreme individual to infinity, while the crowding distance of the intermediate individual is equal to the absolute normalized difference of the objective function values of the two adjacent individuals;
the diversity maintenance mechanism of the external archive is as follows: and when the number of the external file individuals exceeds a set maximum value, deleting the individuals with the minimum crowding distance in the external files until the number of the external file individuals is equal to the preset maximum value.
The invention has the following beneficial effects:
the method adds a disturbance mechanism of a balance optimizer algorithm on the basis of a multi-target grey wolf colony algorithm search mechanism, further expands the search range of a multi-target colony intelligent algorithm, and effectively improves the convergence and the distribution of algorithm search results; compared with the existing group intelligent optimization algorithm, the optimization method provided by the invention can provide better and more diversified power flow voltage optimization schemes for decision makers, and can better meet the decision requirements of the decision makers under different target preferences, so that the scene requirement of the actual power distribution network power flow voltage optimization can be better met.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a power distribution network multi-objective power flow voltage optimization method considering distributed photovoltaic access in an embodiment of the present invention.
Fig. 2 is a schematic diagram of a topology of an IEEE33 node power distribution system according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a daily active power output curve of a distributed photovoltaic apparatus according to an embodiment of the present invention.
Fig. 4 is a diagram illustrating a daily power variation curve of a load node according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of Pareto frontiers obtained by using a multi-objective gray wolf pack algorithm, a multi-objective balance optimizer algorithm, and a proposed multi-objective pack intelligent optimization algorithm optimization embodiment in a specific example of the present invention.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In addition, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, well known means within the skill of those in the art have not been described in detail so as not to obscure the invention.
As shown in fig. 1, a flowchart of a power distribution network multi-objective tidal current and voltage optimization method considering distributed photovoltaic access is provided in an embodiment of the present invention, and a novel multi-objective group intelligent optimization algorithm is provided in combination with a multi-objective grey wolf pack algorithm and a selection and update mechanism of a multi-objective balance optimizer, for the field of tidal current and voltage optimization considering a distributed photovoltaic access scene, where the method mainly includes the following steps S1 to S6:
s1, collecting and processing operation data of each monitoring point in a power distribution network and nameplate parameters of each power device;
specifically, the operation data comprises voltage, current and power of each monitoring point in different periods; each power device comprises a distributed photovoltaic device, a power transformer, a power line and a reactive power compensation device;
s2, performing system modeling on the power distribution network according to the operation data and the nameplate parameters, and establishing a multi-target power flow voltage optimization control model;
specifically, a power distribution network multi-target power flow voltage optimization model considering a distributed photovoltaic access scene is established by taking minimum system loss, minimum average voltage deviation and minimum light rejection rate of distributed photovoltaic equipment as optimization targets, taking taps of on-load tap changers in all time periods, compensation capacity of reactive compensation devices and active and reactive outputs of the distributed photovoltaic equipment as decision variables, and taking power flow balance constraint, system voltage operation constraint and element current operation constraint as constraint conditions;
in order to ensure that the generated power can be effectively utilized in the power distribution network, improve the operating economy of the power distribution network and reduce the loss of the power distribution system, the embodiment of the invention takes the minimum average system loss of the power distribution network as one of the optimization targets in the following form:
Figure BDA0003824319780000071
wherein, N L The total number of power lines in the power system, N t Is the total number of time periods of the model, P loss (t) Total loss of the grid during the tth time periodL is the set of all power lines in the power system, G ij (t) and B ij (t) conductance and susceptance, V, between node i and node j in the node admittance matrix in the tth period, respectively i (t) and θ ij (t) is the effective value of the voltage at the node i and the voltage phase difference between the node i and the node j in the t-th period;
in order to ensure safe and stable operation of the power distribution system, it is required to ensure that the voltage of each node in the power distribution network can be maintained near a rated value, and therefore, the embodiment of the invention takes the minimum average voltage deviation of each node in the power distribution network as an optimization target according to the following form:
Figure BDA0003824319780000072
wherein, V i,base Is the reference voltage at node i, N B Is the total number of nodes, V i * (t) is the voltage per unit value at node i, representing the per unit value;
in order to improve the utilization rate of the distributed photovoltaic equipment and provide clean photovoltaic electric energy to the power distribution network as much as possible, the minimum average light rejection rate of the distributed photovoltaic equipment is taken as one of optimization targets, and the calculation method of the optimization target is that
Figure BDA0003824319780000073
Wherein, P PV,i (t) is the active power actually generated by the ith distributed photovoltaic equipment in the tth time period, P PV,i,max (t) is the maximum active power that the ith distributed photovoltaic equipment can send to the power distribution network in the tth time period, N PV For the total number of distributed photovoltaic devices, T PV For a set of time periods when the maximum active power output of all distributed photovoltaic devices is greater than 0, | T PV L is the set T PV The number of elements contained;
under the scene of considering distributed photovoltaic access, the main mode of power distribution system tide voltage optimization control is mainlyThe method comprises the steps of adjusting the tap of the on-load tap-changing transformer, the compensation capacity of a reactive compensation device, the active and reactive power output of the distributed photovoltaic equipment and the like, so that the embodiment of the invention adopts the transformation ratio K of the on-load tap-changing transformer in each period Ti (t) reactive compensation Q of parallel compensator Ci (t) active power output P of distributed power supply DGi (t) and reactive power Q DGi (t) as a decision variable in the optimization model, and setting upper and lower limit constraints on the decision variable as shown below:
Figure BDA0003824319780000081
wherein N is G 、N T 、N C 、N DG The total number of generator nodes, the total number of adjustable taps of the on-load tap changing transformers, the total number of parallel capacitor groups and the total number of distributed power supply nodes in the power system are sequentially and respectively;
the power distribution system needs to satisfy the following equality constraints to make the total input power equal to the total output power at each node of the distribution network, the constraints are the power flow balance constraints of the power distribution system, and are the main components of the equality constraints of the power flow voltage optimization model of the power distribution network:
Figure BDA0003824319780000082
wherein, P Gi (t) and Q Gi (t) total active power and total reactive power, P, respectively, emitted by the power generation equipment at node i in the tth time period Li (t) and Q Li (t) total active power and total reactive power, Q, respectively, consumed by the load at node i during the t-th time period Ci (t) is the reactive power compensated by the reactive compensation equipment at the node i in the tth time interval;
in order to ensure safe and stable operation of the power distribution system, the embodiment of the invention sets that the voltage at each node of the power distribution network, the total incoming line power factor at each node and the current of each branch circuit meet the inequality constraint shown as follows:
Figure BDA0003824319780000083
wherein, I (i,j) (t) is the current of branch (i, j) during the tth time period;
further, the multi-objective power flow voltage optimization model can be abstracted into the form shown as follows:
Figure BDA0003824319780000091
wherein y is a target vector consisting of m optimization targets, x is a decision vector with dimension n,xand
Figure BDA0003824319780000092
lower and upper bounds, g, of the decision vector, respectively i (x) For defining an inequality constraint, h j (x) The method is used for defining equality constraints, p is the number of all inequality constraints, and q is the number of all equality constraints;
s3, setting initial parameters of a tidal current and voltage optimization algorithm, carrying out group initialization to obtain an initial group, calculating a target function value of each individual in the initial group, and judging whether the target function value meets constraint conditions or not; selecting individuals which are not dominated by other individuals from the initial population according to a multi-target Pareto domination principle and storing the individuals in an external file;
specifically, the step S3 includes:
generating initial groups with a certain number of individuals randomly and possibly in a range constrained by upper and lower limits of decision variables based on a Monte Carlo method, wherein each individual in the initial groups corresponds to a tidal current voltage control strategy;
after the initial group is generated, power flow and voltage distribution under each power flow voltage control strategy are obtained through power flow calculation, and then corresponding objective function values are calculated, and whether the objective function values meet the preset requirements of safe and stable operation of the power system is judged;
comparing all individuals in the initial population according to a Pareto domination mechanism and objective function values of the individuals, and storing all individuals not dominated by other individuals in the initial population into the external archive according to a comparison result;
specifically, the embodiment of the invention introduces a multi-target Pareto domination mechanism, and sets the set X f For a set composed of all feasible solutions in the n-dimensional decision space, defining a Pareto domination relationship as follows: for x 1 ,x 2 ∈X f If and only if pairs
Figure BDA0003824319780000093
All have f i (x 1 )≤f i (x 2 ) And is made of
Figure BDA0003824319780000094
So that f i (x 1 )<f i (x 2 ) At this time, it is called x 1 Dominating x 2 Is recorded as
Figure BDA0003824319780000096
Further, the Pareto optimal solution is defined as: for x * ∈X f And if and only if
Figure BDA0003824319780000095
So that
Figure BDA0003824319780000097
Then, it is called x * Is a Pareto optimal solution, also called a non-inferior solution or a non-dominant solution; furthermore, a set composed of all Pareto optimal solutions is called a Pareto optimal solution set, and can also be called a non-inferior solution set or a non-dominant solution set; then, defining the mapping of the Pareto optimal solution set to a target space as a Pareto front edge;
s4, selecting elite individuals from the external archive, and performing population updating according to a preset population updating algorithm to obtain a new generation of population; calculating the objective function value of each individual in the new generation group, and judging whether the objective function value meets the constraint condition;
specifically, the step S4 includes:
dividing a target space into grids consisting of a plurality of hypercubes by using a sorting method based on a self-adaptive grid, screening the hypercubes in the grids by using a roulette selection method, and screening an individual from the screened hypercubes randomly and possibly; wherein the fewer the number of individuals that the hypercube contains that are not dominated by other individuals, the higher the probability that the hypercube is screened;
repeating the steps for 3 times according to an elite individual selection mechanism of a grey wolf group algorithm, screening 3 individuals from the external file to be used as elite individuals, and respectively naming the elite individuals as an alpha individual, a beta individual and a delta individual;
after the screening of the elite individuals is finished, updating the decision variable of each individual in the group by taking the position of the elite individual as a reference, thereby achieving the purpose that the search result gradually approaches to the optimal result; in order to improve convergence and distribution of algorithm search, the embodiment of the invention combines the advantages of the gray wolf pack algorithm and the balance optimizer algorithm in algorithm search performance, and provides a preset group update method of a hybrid update mechanism for group update, which is shown as follows, wherein the update method is as follows:
Figure BDA0003824319780000101
wherein K is the current iteration number of the algorithm, and K is the maximum iteration number of the algorithm; x (k) and X (k + 1) are n-dimensional decision variables corresponding to any individual in the population of the kth generation and the population of the kth +1 generation respectively, and X α (k)、X β (k)、X δ (k) N-dimensional decision variables corresponding to the alpha individual, the beta individual and the delta individual screened after the kth time are respectively selected; c 1 、C 2 、C 3 N-dimensional random vectors corresponding to alpha, beta and delta individuals, respectively, and a compliance interval [ -2,2]Uniformly distributed; a. The 1 、A 2 、A 3 N-dimensional random vectors corresponding to alpha, beta and delta individuals respectively, and obedience intervals [ -a, a [ -a]Uniformly distributed; a satisfies the relationship
Figure BDA0003824319780000111
The value of which decreases as the number of iterations increases; lambda α 、λ β 、λ δ N-dimensional random vectors corresponding to the alpha individual, the beta individual and the delta individual are respectively and uniformly distributed according to units; f α 、F β 、F δ N-dimensional random vectors corresponding to the alpha individual, the beta individual and the delta individual respectively satisfy the relationship
Figure BDA0003824319780000112
Figure BDA0003824319780000113
r is an n-dimensional random vector that is uniformly distributed in units,
Figure BDA0003824319780000114
is the product of the element group operations; g α 、G β 、G δ N-dimensional random vectors corresponding to the alpha individual, the beta individual and the delta individual respectively satisfy the relation shown in the formula (9);
Figure BDA0003824319780000115
wherein r is 1 And r 2 All are random numbers obeying unit uniform distribution;
s5, screening out individuals which are not dominated by other individuals from the new generation group according to a multi-Pareto domination principle, storing the individuals into the external file, and updating the external file; and ensuring that the number of the individuals of the external files does not exceed a preset maximum value all the time through a diversity maintenance mechanism;
specifically, the update principle of the external file is as follows: comparing each non-dominated individual in the current population with all individuals in an external archive according to a Pareto domination principle, deleting all dominated individuals in the external archive, and storing a new generation of individuals which are not dominated by all individuals in the external archive into the external archive; calculating the crowding distance of each individual in the external archive based on a crowding distance calculation mechanism, and adopting the diversity of the external archive to carry out a maintenance mechanism to prevent the data capacity of the external archive from overflowing; specifically, if the external file is empty, a new generation of individuals is directly stored into the external file; if the new generation of individuals are dominated by the individuals in the external archive, directly discarding the new generation of individuals; if the new generation of individuals dominates part of individuals in the external file, discarding all dominated individuals in the original external file and storing the new generation of individuals into the external file; if the new generation of individuals and all the individuals in the external file are not dominant, reserving all the individuals in the external file and storing the new generation of individuals in the external file;
the method for estimating the individual density is a key for influencing the distribution performance of the search results of the multi-objective optimization algorithm, and in order to improve the distribution universality and uniformity of the search results of the multi-objective optimization algorithm in a target space, the method adopts a calculation method based on the crowding distance to estimate the individual density of an external archive; the calculation mechanism of the congestion distance is as follows: all the individuals in the external files are ranked according to the size of the objective function value, the individual with the minimum function value or the maximum function value is regarded as an extreme individual, and the other individuals are regarded as intermediate individuals; setting the crowding distance of the extreme individual to infinity, while the crowding distance of the intermediate individual is equal to the absolute normalized difference of the objective function values of the two adjacent individuals;
the diversity maintenance mechanism of the external archive is as follows: when the number of the external file individuals exceeds a set maximum value, deleting the individuals with the minimum crowding distance in the external files until the number of the external file individuals is equal to the preset maximum value;
and S6, judging whether a preset iteration termination condition is met, if the preset iteration termination condition is not met, turning to the step S3 to continue the iteration, and if the preset iteration termination condition is met, stopping the iteration and obtaining a final power flow voltage optimization control scheme from the external file.
The following describes a specific application process of the method according to an embodiment of the present invention with reference to a specific example:
(1) Collecting and processing operation data of each monitoring point in the power distribution network and nameplate parameters of each power device;
the example selects an IEEE33 node power distribution network system as an example system. An IEEE33 node system is a typical regional three-phase radial distribution system, and consists of 33 nodes and 32 branches, wherein the system comprises 1 distribution network source node and 32 load nodes, the voltage level of the whole system is 12.66kV, the total load of the network is 3750kw + j2300kvar, the voltage per unit value of the source node is 1.05, the schematic diagram of the topological structure is shown in fig. 2, and the parameters of the nodes and the branches are shown in table 1; according to the national standard Q/GDW 480-2010: according to the requirements of technical regulations on the distributed power supply access to the power grid, the total access capacity of the distributed power supply of the power distribution network cannot exceed 25% of the maximum load in the power supply area of the upper-level transformer, so that the access capacity of photovoltaic equipment on each node is configured to be 200kW, and the distributed photovoltaic equipment is installed at nodes 8, 25 and 32 in a predetermined manner; for the convenience of result analysis, in this example, the daily active power curves of the distributed photovoltaic devices are the same, and the daily power change curves of the load nodes are also the same, and the daily active power curves of the distributed photovoltaic devices and the daily power change curves of the load nodes are respectively shown in fig. 3 to 4.
TABLE 1-node and tributary parameters for IEEE33 node System
Figure BDA0003824319780000131
(2) Carrying out system modeling on the distribution network according to the operation data and the nameplate parameters, and establishing a multi-target tidal current voltage optimization control model;
in this example, the distributed photovoltaic device is installed at nodes 8, 25, and 32, and operates in a unit power factor state, the maximum active power curve in each time period is as shown in fig. 4, and the active power generated by the distributed photovoltaic device in each time period can be continuously adjusted in a range from 0 to the maximum active power; selecting nodes 2, 4, 7, 8, 14, 23, 24, 25, 29, 30, 31 and 32 with larger reactive power shortage as installation nodes of reactive compensation equipment, and setting the reactive compensation equipment to be continuously adjustable, wherein the compensation ranges are respectively 0-60 kvar, 0-80 kvar, 0-100 kvar, -100 kvar, 0-80 kvar, 0-50 kvar, 0-200 kvar, -200 kvar, 0-70 kvar, 0-100 kvar, 0-70 kvar and-100 kvar; according to the national standard Q/GDW 480-2010: according to the requirements of technical provisions for accessing the distributed power supply to a power grid, the voltage per unit value at each node is limited to 0.93-1.07 p.u., and the power factor of an incoming line at each node is not less than 0.9; on the basis, establishing an IEEE33 node power distribution system three-target power flow voltage optimization control model under a distributed photovoltaic access scene by taking the minimum loss of a power distribution network average system, the minimum deviation of the average voltage of each node and the minimum average light rejection rate of distributed photovoltaic as optimization targets in the whole period of the power distribution network;
(3) Setting initial parameters of a tidal current and voltage optimization algorithm, carrying out group initialization, calculating a target function value of each individual in an initial group, judging whether the target function value meets constraint conditions or not, and selecting non-dominated individuals from the initial group according to a multi-target Pareto domination principle to store the non-dominated individuals in an external file;
in this example, the maximum iteration number of the power flow voltage optimization algorithm is set to be 200, and the maximum scale of the group scale and the maximum scale of the external archive are both set to be 200; 200 initial groups are randomly generated in a range of decision variable upper and lower limit constraints and the like by adopting a Monte Carlo-based method, wherein each individual in the groups corresponds to each tidal current voltage control strategy; after the initial group is generated, power flow and voltage distribution under each power flow voltage control strategy are obtained through power flow calculation, and then corresponding objective function values are calculated, and whether the objective function values meet the requirements of safe and stable operation of the power system is judged; introducing a Pareto domination mechanism to compare all individuals in the initial population according to the objective function values, and storing all the individuals which are not dominated by other individuals in the initial population, namely non-dominated individuals in the initial population into an external file;
(4) Selecting elite individuals from an external archive by combining a grey wolf pack algorithm and a selection mechanism of a balance optimizer, obtaining a new generation group according to the proposed group updating method, then calculating a target function value of each individual in the new generation group, and judging whether the target function value meets a constraint condition or not;
in this example, a three-dimensional target space is divided into grids composed of 10 × 10 × 10 hypercubes by using a sorting method based on an adaptive grid, the hypercubes in the grids are screened by a roulette selection method, the probability that the hypercube contains less non-dominant individuals is higher, and then the individuals are screened out from the hypercubes at random and possibly; repeating the steps for three times, screening three individuals from external files to be used as elite individuals, and respectively naming the elite individuals as alpha individuals, beta individuals and delta individuals; then, each individual in the population is updated using the proposed population update mechanism, and the update method is as follows:
Figure BDA0003824319780000141
Figure BDA0003824319780000151
Figure BDA0003824319780000152
Figure BDA0003824319780000153
wherein: k is the current iteration number of the algorithm, and K is the maximum iteration number of the algorithm; x (k) and X (k + 1) are n-dimensional decision variables corresponding to any individual in the k generation population and the k +1 generation population respectively, and X α (k)、X β (k)、X δ (k) N-dimensional decision variables corresponding to the alpha individual, the beta individual and the delta individual screened after the kth time are respectively; c is an n-dimensional random vector, and the obedience interval is [ -2,2]Uniform distribution of the components; a is an n-dimensional random vector, obedience interval [ -a, a [)]Uniform distribution of the components; a satisfies the relationship
Figure BDA0003824319780000154
The value of which depends on the number of iterationsIncrease and decrease; lambda is an n-dimensional random vector and is uniformly distributed according to units; f is an n-dimensional random vector and satisfies the relation
Figure BDA0003824319780000155
r is an n-dimensional random vector that is uniformly distributed in units,
Figure BDA0003824319780000156
is the product of the element group operations; g is an n-dimensional random vector and satisfies the following relation
Figure BDA0003824319780000157
Figure BDA0003824319780000158
Wherein r is 1 And r 2 Are random numbers that are uniformly distributed in units.
(5) Screening independent individuals from a new generation group according to a Pareto domination principle, updating external files, and ensuring that the scale of the external files does not exceed a preset maximum value all the time through a diversity maintenance mechanism;
updating of the external file: if the external file is empty, directly storing a new generation of individuals into the external file; if the new generation of individuals are dominated by the individuals in the external files, directly discarding the new generation of individuals; if the new generation of individuals dominates part of individuals in the external file, all dominated individuals in the original external file are discarded, and the new generation of individuals are stored in the external file; if the new generation of individuals and all the individuals in the external file are not dominant, keeping all the individuals in the external file and storing the new generation of individuals in the external file;
estimation of individual density: the individual density estimation of the external files is carried out by adopting a calculation method based on the crowding distance;
and (3) maintaining the diversity of the external files: when the individual scale of the external file exceeds the set maximum value of 200, deleting the individual with the minimum crowding distance in the external file, so that the individual scale of the external file is always within 200;
(6) Judging whether iteration termination conditions are met, if the iteration termination conditions are not met, turning to the third step to continue, if the iteration termination conditions are met, stopping iteration and obtaining a final tide voltage optimization control scheme from an external file;
in order to verify that the proposed multi-target optimization algorithm has excellent searching performance, the solving results of the multi-target grey wolf colony algorithm and the multi-target balance optimizer algorithm are compared with the solving result of the proposed multi-target colony intelligent optimization algorithm in the embodiment. The Pareto frontier obtained by the three multi-objective group intelligent optimization algorithms is shown in fig. 5, and it can be known from fig. 5 that: pareto front edges obtained by multi-target grey wolf colony algorithm searching are uniformly distributed near the intermediate solution, but the whole distribution range on a target space is not wide enough; the distribution range of the Pareto front edge obtained by utilizing the multi-objective balance optimizer algorithm in the three-dimensional target space is too narrow, the target function values corresponding to the external files are all concentrated near the intermediate solution, and the spatial distribution is not uniform; the searching capability of the multi-target optimization algorithm on the extreme solution is stronger than that of the multi-target grey wolf colony algorithm and the multi-target balance optimizer algorithm, and the searching results can be widely and uniformly distributed in a target space. In conclusion, the distribution performance of the proposed multi-target group intelligent optimization algorithm is obviously improved compared with the existing multi-target algorithm, so that the scene requirement of multi-target power flow voltage optimization of the actual power distribution network can be better met.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and alterations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the market, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (6)

1. A multi-objective power flow voltage optimization method for a power distribution network considering distributed photovoltaic access is characterized by comprising the following steps:
s1, collecting and processing operation data of each monitoring point in a power distribution network and nameplate parameters of each power device;
s2, performing system modeling on the power distribution network according to the operation data and the nameplate parameters, and establishing a multi-target power flow voltage optimization control model;
s3, setting initial parameters of a tidal current and voltage optimization algorithm, carrying out group initialization to obtain an initial group, calculating a target function value of each individual in the initial group, and judging whether the target function value meets constraint conditions or not; selecting individuals which are not dominated by other individuals from the initial population according to a multi-target Pareto domination principle and storing the individuals in an external archive;
s4, selecting elite individuals from the external files, and performing population updating according to a preset population updating algorithm to obtain a new generation of population; calculating the objective function value of each individual in the new generation group, and judging whether the objective function value meets the constraint condition;
s5, screening out individuals which are not dominated by other individuals from the new generation group according to a multi-Pareto domination principle, storing the individuals into the external file, and updating the external file; and ensuring that the number of the individuals of the external files does not exceed a preset maximum value all the time through a diversity maintenance mechanism;
and S6, judging whether a preset iteration termination condition is met, if the preset iteration termination condition is not met, turning to the step S3 to continue the iteration, and if the preset iteration termination condition is met, stopping the iteration and obtaining a final power flow voltage optimization control scheme from the external file.
2. The multi-objective power flow voltage optimization method for the power distribution network considering the distributed photovoltaic access as claimed in claim 1, wherein the operation data includes voltage, current and power of each monitoring point at different time periods; each power device comprises a distributed photovoltaic device, a power transformer, a power line and a reactive power compensation device.
3. The method for optimizing the multi-objective power flow voltage of the power distribution network considering the distributed photovoltaic access according to claim 2, wherein the step S2 comprises the following steps:
the method comprises the steps of taking minimum system loss, minimum average voltage deviation and minimum light rejection rate of distributed photovoltaic equipment as optimization targets, taking taps of on-load tap changers in all time periods, compensation capacity of reactive compensation devices and active and reactive power output of the distributed photovoltaic equipment as decision variables, taking power flow balance constraint, system voltage operation constraint and element current operation constraint as constraint conditions, and establishing a multi-target power flow voltage optimization model considering a distributed photovoltaic access scene.
4. The method for optimizing the multi-objective power flow voltage of the power distribution network considering the distributed photovoltaic access according to claim 3, wherein the step S3 comprises the following steps:
generating initial groups with a certain number of individuals randomly and possibly in a range constrained by upper and lower limits of decision variables based on a Monte Carlo method, wherein each individual in the initial groups corresponds to a tidal current voltage control strategy;
after the initial group is generated, power flow and voltage distribution under each flow voltage control strategy are obtained through flow calculation, and then corresponding objective function values are calculated, and whether the objective function values meet the preset requirements of safe and stable operation of the power system is judged;
and comparing all the individuals in the initial population according to a Pareto domination mechanism and the objective function values of the individuals, and storing all the individuals not dominated by other individuals in the initial population into the external archive according to a comparison result.
5. The method for optimizing the multi-objective power flow voltage of the power distribution network considering the distributed photovoltaic access according to claim 4, wherein the step S4 comprises:
dividing a target space into grids consisting of a plurality of hypercubes by using a sorting method based on an adaptive grid, screening the hypercubes in the grids by a roulette selection method, and screening an individual from the screened hypercubes randomly and possibly; wherein the fewer the number of individuals contained by the hypercube that are not subject to other individuals, the higher the probability that the hypercube is screened;
repeating the steps for 3 times, screening 3 individuals from the external files as elite individuals, and respectively naming the elite individuals as alpha individuals, beta individuals and delta individuals;
updating each individual in the population by using a preset population updating method, wherein the updating method is as follows:
Figure FDA0003824319770000021
Figure FDA0003824319770000022
Figure FDA0003824319770000023
Figure FDA0003824319770000024
Figure FDA0003824319770000031
Figure FDA0003824319770000032
Figure FDA0003824319770000033
wherein K is the current iteration number of the algorithm, and K is the maximum iteration number of the algorithm; x (k) and X (k + 1) are n-dimensional decision variables corresponding to any individual in the population of the kth generation and the population of the kth +1 generation respectively, and X α (k)、X β (k)、X δ (k) N-dimensional decision variables corresponding to the alpha individual, the beta individual and the delta individual screened after the kth time are respectively selected; c 1 、C 2 、C 3 N-dimensional random vectors corresponding to alpha individual, beta individual and delta individual, respectively, and a compliance interval [ -2,2]Uniform distribution of the components; a. The 1 、A 2 、A 3 N-dimensional random vectors corresponding to alpha, beta and delta individuals respectively, and obedience intervals [ -a, a [ -a]Uniform distribution of the components; a satisfies the relationship
Figure FDA0003824319770000034
The value of which decreases as the number of iterations increases; lambda [ alpha ] α 、λ β 、λ δ N-dimensional random vectors corresponding to the alpha individual, the beta individual and the delta individual respectively are uniformly distributed according to units; f α 、F β 、F δ N-dimensional random vectors corresponding to the alpha individual, the beta individual and the delta individual respectively satisfy the relationship
Figure FDA0003824319770000035
Figure FDA0003824319770000036
r is an n-dimensional random vector that is uniformly distributed in units,
Figure FDA0003824319770000037
is the product of the element group operations; g α 、G β 、G δ N-dimensional random vectors corresponding to the alpha individual, the beta individual and the delta individual respectively satisfy the relation shown in the formula (9);
Figure FDA0003824319770000038
Figure FDA0003824319770000039
GCP＝0.5r 1 ×(r 2 ≥0.5)×r
Figure FDA00038243197700000310
Figure FDA00038243197700000311
GCP＝0.5r 1 ×(r 2 ≥0.5)×r
Figure FDA00038243197700000312
Figure FDA00038243197700000313
GCP＝0.5r 1 ×(r 2 ≥0.5)×r
wherein r is 1 And r 2 Are random numbers that are uniformly distributed in units.
6. The method for optimizing the multi-objective power flow voltage of the power distribution network considering the distributed photovoltaic access according to claim 5, wherein the step S5 comprises:
the updating principle of the external file is as follows: comparing each non-dominated individual in the current population with all individuals in an external archive according to a Pareto domination principle, deleting all dominated individuals in the external archive, and storing a new generation of individuals which are not dominated by all individuals in the external archive into the external archive; calculating the crowding distance of each individual in the external archive by using a crowding distance calculation mechanism, and preventing the data capacity of the external archive from overflowing by using the external archive diversity maintenance mechanism; wherein the congestion distance calculation mechanism is: all the individuals in the external files are ranked according to the size of the objective function value, the individual with the minimum function value or the maximum function value is regarded as an extreme individual, and the other individuals are regarded as intermediate individuals; setting the crowding distance of the extreme individual to infinity, while the crowding distance of the intermediate individual is equal to the absolute normalized difference of the objective function values of the two adjacent individuals;
the diversity maintenance mechanism of the external archive is as follows: and when the number of the external file individuals exceeds the set maximum value, deleting the individuals with the minimum crowding distance in the external files until the number of the external file individuals is equal to the preset maximum value.
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