CN108695854B - Multi-target optimal power flow control method, device and equipment for power grid - Google Patents

Multi-target optimal power flow control method, device and equipment for power grid Download PDF

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CN108695854B
CN108695854B CN201810649906.4A CN201810649906A CN108695854B CN 108695854 B CN108695854 B CN 108695854B CN 201810649906 A CN201810649906 A CN 201810649906A CN 108695854 B CN108695854 B CN 108695854B
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power
generator
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CN108695854A (en
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刘文彬
杨春
温柏坚
梅发茂
王泽涌
伍斯龙
佟忠正
吴赟
王哲
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Guangdong Power Grid Co Ltd
Information Center of Guangdong Power Grid 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected 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
    • H02J3/48—Controlling the sharing of the in-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
    • 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
    • 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]

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Abstract

The invention discloses a multi-target optimal power flow control method for a power grid, which comprises the steps of firstly determining a target function and a constraint condition of the power grid, and then determining a power flow equation corresponding to the power grid according to the target function and the constraint condition; finally, solving a power flow equation by using a multi-target evolution algorithm based on decomposition to obtain an optimal power grid configuration parameter corresponding to a target function; wherein the objective function of the grid is the minimum generation cost of the generator and the minimum active power consumption of the transmission line. Therefore, the minimum power generation cost and the minimum active power loss are used as the objective function, so that the finally solved power grid configuration parameters can meet the condition that the power generation cost is minimum and the condition that the active power of a transmission line is minimum, and the power loss in the power grid system is reduced while the power generation cost in the power grid system is minimum. In addition, the invention also discloses a multi-target optimal power flow control device and equipment for the power grid, and the effects are as above.

Description

Multi-target optimal power flow control method, device and equipment for power grid
Technical Field
The invention relates to the technical field of electric power, in particular to a multi-target optimal power flow control method, device and equipment for a power grid.
Background
The multi-target optimal power flow control is an important tool for realizing stable and efficient operation of a modern power grid system. For a given power grid system, the multi-objective optimal power flow control problem is to find a group of power grid configurations, such as the obtained active power of each slack node, the voltage of a load node, the reactive power of a generator and the load of a transmission line, so that a specific objective function can be optimized under the condition of meeting a series of constraint conditions. The power grid configuration needing optimization in the multi-objective optimal power flow control comprises both continuous control parameters and discrete control parameters. The constraints for optimization include power flow equations, system safety, and equipment operating limitations.
The optimization objective widely adopted in the traditional multi-objective optimal power flow control is to minimize the power generation cost, namely, the current multi-objective optimal power flow control only considers a single objective, only a single optimization result can be obtained after the single objective is optimized, and the optimization is carried out by taking the single optimization objective as a reference, but other factors in a power grid system are not optimized, such as active power loss and the like; thus, only the optimization goal of minimizing the power generation cost is considered, and although the obtained power grid configuration parameters minimize the power generation cost, the minimum power loss in the power grid system cannot be guaranteed.
Therefore, reducing power loss in a grid system while ensuring that the cost of generating power in the grid system is minimized is a problem that needs to be addressed by those skilled in the art.
Disclosure of Invention
The invention aims to provide a multi-target optimal power flow control method, a multi-target optimal power flow control device and multi-target optimal power flow control equipment for a power grid, which are used for reducing power loss in the power grid system while ensuring the minimum power generation cost in the power grid system.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention discloses a multi-target optimal power flow control method for a power grid, which comprises the following steps:
determining an objective function, a constraint condition and a control variable of a power grid;
determining a power flow equation corresponding to the power grid according to the target function, the control variable and the constraint condition;
solving the power flow equation by using a multi-target evolution algorithm based on decomposition to obtain an optimal power grid configuration parameter corresponding to the target function;
wherein the objective function of the grid comprises a minimum cost of power generation by the generator and a minimum active power loss of the transmission line.
Preferably, the determining the objective function, the constraint condition and the control variable of the power grid comprises:
taking the active power of the generator and the terminal voltage of the generator as the control variables;
determining a cost coefficient of the generator and a conductance of the transmission line;
determining a minimum power generation cost of the generator using the active power of the generator and a cost coefficient of the generator;
determining the minimum active power loss of the transmission line by using the voltage of the load nodes at two ends of the transmission line and the conductance of the transmission line;
determining that a terminal voltage of the generator is between a minimum operating voltage of the generator and a maximum operating voltage of the generator as a first constraint condition;
determining that the active power of the generator is in a second constraint condition between the minimum active power of the generator and the maximum active power of the generator;
determining that the reactive power of the generator is between the minimum reactive power of the generator and the maximum reactive power of the generator as a third constraint condition;
determining that the voltage of the load node on the transmission line is between the minimum load voltage and the maximum load voltage as a fourth constraint condition;
determining that the load of the transmission line does not exceed a maximum load amount is a fifth constraint.
Preferably, the control variables further include: a transformer tap of the grid, a susceptance of a parallel capacitor of the grid;
correspondingly, the constraint condition further includes: a sixth constraint condition that the transformer gear of the power grid is between the minimum transformer gear and the maximum transformer gear is set;
it is a seventh constraint that the reactive power of the parallel capacitor is between the minimum reactive power of the parallel capacitor and the maximum reactive power of the parallel capacitor.
Preferably, the solving the power flow equation by using a multi-target evolution algorithm based on decomposition to obtain the optimal power grid configuration parameter corresponding to the objective function includes:
initializing the power flow equation to randomly generate a first solution set;
and iterating the solutions in the first solution set until an optimal solution corresponding to the objective function is obtained, wherein the optimal solution is the optimal power grid configuration parameter.
Preferably, the iterating the solutions in the first solution set until obtaining the optimal solution corresponding to the objective function includes:
crossing and mutating each solution in the first solution set to obtain a second solution set;
merging the first solution set and the second solution set to obtain a third solution set;
and carrying out target space clustering according to the weight vector set and the third solution set which are randomly generated during the initialization of the power flow equation until an optimal solution corresponding to the target function is obtained.
Preferably, the performing target spatial clustering according to the set of weight vectors randomly generated during the initialization of the power flow equation and the third solution set includes:
normalizing each solution in the third set of solutions to obtain a target value corresponding to each solution in the third set of solutions;
calculating a Euclidean distance between the target value and each weight vector in the weight vector set;
determining a target Euclidean distance and a target weight vector corresponding to the target Euclidean distance from each Euclidean distance;
assigning the target value to a cluster corresponding to the target weight vector;
and selecting a target solution from the clustering cluster and distributing the target solution to a next generation solution set until an optimal solution corresponding to the target function is obtained.
Preferably, the determining a target euclidean distance from each of the euclidean distances comprises:
and selecting the shortest Euclidean distance from the Euclidean distances as the target Euclidean distance.
Preferably, the selecting a target solution from the cluster comprises:
determining a violation degree value of a solution in the cluster to the constraint condition;
calculating Chebyshev distances of solutions in the cluster to an origin along the target weight vector by using a scalar function;
judging whether the violation range value meets a first condition and whether the Chebyshev distance meets a second condition;
and taking the solution which simultaneously meets the first condition and the second condition as a target solution in the clustering cluster.
Secondly, the present invention discloses an optimal control apparatus for a power grid, comprising:
the determining module is used for determining an objective function, a constraint condition and a control variable of the power grid;
the power flow equation determining module is used for determining a power flow equation corresponding to the power grid according to the target function, the control variable and the constraint condition;
the solving module is used for solving the power flow equation by using a multi-target evolution algorithm based on decomposition to obtain an optimal power grid configuration parameter corresponding to the target function;
wherein the objective function of the grid comprises a minimum cost of power generation by the generator and a minimum active power loss of the transmission line.
Third, the present invention discloses an optimal control apparatus for a power grid, comprising:
a memory for storing a computer program;
a processor for executing the computer program stored in the memory to implement the steps of the multi-objective optimal power flow control method for an electrical grid as described in any one of the above.
The method comprises the steps of firstly determining a target function and a constraint condition of the power grid, and then determining a power flow equation corresponding to the power grid according to the target function and the constraint condition; finally, solving a power flow equation by using a multi-target evolution algorithm based on decomposition to obtain an optimal power grid configuration parameter corresponding to a target function; wherein the objective function of the grid is the minimum generation cost of the generator and the minimum active power consumption of the transmission line. Therefore, the minimum power generation cost and the minimum active power loss are used as the objective function, so that the finally solved power grid configuration parameters can meet the condition that the power generation cost is minimum and the condition that the active power of a transmission line is minimum, and the power loss in the power grid system is reduced while the power generation cost in the power grid system is minimum. In addition, the invention also discloses a multi-target optimal power flow control device and equipment for the power grid, and the effects are as above.
Drawings
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 schematic flow chart of a multi-objective optimal power flow control method for a power grid according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a multi-objective optimal power flow control device for a power grid according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a multi-objective optimal power flow control device for a power grid according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a multi-target optimal power flow control method, a multi-target optimal power flow control device and multi-target optimal power flow control equipment for a power grid, which are used for reducing power loss in the power grid system while ensuring the minimum power generation cost in the power grid system.
Referring to fig. 1, fig. 1 is a schematic flow chart of a multi-objective optimal power flow control method for a power grid according to an embodiment of the present invention, where the method includes:
s101, determining an objective function, a constraint condition and a control variable of the power grid.
Specifically, in this embodiment, the objective function in the power grid system includes: in the present embodiment, two objective functions, that is, the minimum power generation cost of the generator in the power grid system and the minimum active power loss of the transmission line, are mainly considered. The minimum cost of power generation for a generator can be expressed by:
Figure BDA0001704461830000051
wherein N isGNumber of generators in the grid system, PGiActive power of the ith generator in the grid system, ai，bi，ciIs the cost factor of the ith generator, ai，bi，ciThe value can be determined according to the type of the generator and the parameters and value of the generator, and the embodiment of the invention aims at the step ai，bi，ciThe size of the cost factor of (2) is not limited.
Further, the minimum active power loss for the transmission line can be calculated using the following equation:
Figure BDA0001704461830000061
wherein, gkFor the conductance of the kth transmission line in the grid system, NlThe number of transmission lines in the power grid system; i and j denote two end nodes, V, on the k-th transmission lineiAnd VjThe voltage values, delta, of the two end nodes on the kth transmission line are indicatedijThe difference between the amplitudes of the voltages at the two end nodes on the kth transmission line is indicated. It should be noted that, in this embodiment, the objective function in the power grid may be other than the two mentioned in this embodiment, and the embodiment of the present invention is not limited herein.
Further, the control variable may be the active power of the generator, the terminal voltage of the generator, the transformer gear of the grid, the susceptance of the parallel capacitor of the grid, etc., in addition to the slack node. The constraint conditions include: a power flow equation constraint, a generator constraint, a security constraint for a load node in the grid, a load constraint for a transmission line, a constraint for a grid transformer, and a constraint for a parallel capacitor in the grid.
As a preferred embodiment, step S102 includes:
taking the active power of the generator and the terminal voltage of the generator as control variables;
determining a cost coefficient of the generator and a conductance of the transmission line;
and determining the minimum power generation cost of the generator by using the active power of the generator and the cost coefficient of the generator.
And determining the minimum active power loss of the transmission line by using the voltage variable of the load nodes at two ends of the transmission line and the conductance of the transmission line.
Determining that the terminal voltage of the generator is between the minimum operating voltage of the generator and the maximum operating voltage of the generator is a first constraint.
And determining that the active power of the generator is between the minimum active power and the maximum active power of the generator as a second constraint condition.
It is determined that the reactive power of the generator is between the minimum reactive power and the maximum reactive power of the generator as a third constraint.
Determining that the voltage of the load node on the transmission line is between the minimum load voltage and the maximum load voltage is a fourth constraint.
Determining that the load of the transmission line does not exceed the maximum load amount is a fifth constraint.
Further, in consideration of the multi-factor problem in the power grid system, the obtained final power grid configuration parameter is made to be better, and as a preferred embodiment, the control variables further include: transformer gear of electric wire netting, the susceptance of the parallel capacitor of electric wire netting, correspondingly, the constraint condition still includes: the transformer gear of the power grid is between the minimum transformer gear and the maximum transformer gear, and the reactive power ratio of the parallel capacitor is between the minimum reactive power of the parallel capacitor and the maximum reactive power of the parallel capacitor.
In particular, in the present embodiment, the minimum power generation cost and the minimum active power loss can be referred to the above description. The first constraint, the second constraint and the third constraint are constraints on the generator. Wherein, for the first constraint condition, it can be represented by the following formula:
Figure BDA0001704461830000071
wherein, VGiTerminal voltage of the i-th generator, NGTo the number of generators in the grid system,
Figure BDA0001704461830000072
is the minimum operating voltage of the ith generator,
Figure BDA0001704461830000073
the maximum operating voltage of the ith generator. Wherein the content of the first and second substances,
Figure BDA0001704461830000074
and
Figure BDA0001704461830000075
can be determined according to the specific model of the generator and the actual environment of the power grid, for
Figure BDA0001704461830000076
And
Figure BDA0001704461830000077
the value of (b) is not limited herein.
For the second constraint, it can be represented by the following equation:
Figure BDA0001704461830000078
wherein, PGiFor the active power of the i-th generator in the grid system,
Figure BDA0001704461830000079
is the minimum active power of the ith generator,
Figure BDA00017044618300000710
of the i-th generatorMaximum active power, NGIs the number of generators in the grid system. Wherein the content of the first and second substances,
Figure BDA00017044618300000711
and
Figure BDA00017044618300000712
can be determined according to the specific model of the generator and the actual environment of the power grid, for
Figure BDA00017044618300000713
And
Figure BDA00017044618300000714
the size of the present invention is not limited herein.
For the third constraint, it can be represented by the following equation:
Figure BDA00017044618300000715
wherein Q isGiFor the reactive power of the i-th generator in the grid system,
Figure BDA00017044618300000716
is the minimum reactive power of the ith generator,
Figure BDA00017044618300000717
the maximum reactive power of the ith generator. N is a radical ofGIs the number of generators in the grid system.
Figure BDA00017044618300000718
And
Figure BDA00017044618300000719
can be determined according to the specific model of the generator and the actual environment of the power grid, for
Figure BDA00017044618300000720
And
Figure BDA00017044618300000721
the size of the present invention is not limited herein.
For the fourth constraint, it can be represented by the following equation:
Figure BDA00017044618300000722
wherein, VLiFor the voltage at the ith load node in the grid system,
Figure BDA00017044618300000723
is the minimum load voltage of the ith load node,the maximum load voltage of the ith load node; n is a radical ofPQThe number of load nodes in the power grid.
Figure BDA0001704461830000081
Andcan be determined according to the actual environment of the power grid, for
Figure BDA0001704461830000083
And
Figure BDA0001704461830000084
the size of the present invention is not limited herein.
For the fifth constraint, it can be represented by the following equation:
Figure BDA0001704461830000085
wherein S isliIs the load of the i-th transmission line,
Figure BDA0001704461830000086
is the maximum load capacity, N, that can be carried on the ith transmission linelThe number of all transmission lines in the grid.
For the sixth constraint, it can be represented by the following formula:
Ti min≤Ti≤Ti max,i＝1,...,nt
wherein, TiIs the i-th transformer gear, Ti minIs the minimum transformer gear, T, of the ith transformeri maxIs the maximum transformer gear of the ith transformer, ntThe number of transformers. T isi minAnd Ti maxCan be determined according to the actual environment of the power grid, for Ti minAnd Ti maxThe size of the present invention is not limited herein.
For the seventh constraint, it can be represented by the following formula:
Figure BDA0001704461830000087
wherein Q isciRepresenting the reactive power of the ith parallel capacitor,
Figure BDA0001704461830000088
representing the minimum reactive power of the ith parallel capacitor,
Figure BDA0001704461830000089
representing the maximum reactive power of the ith parallel capacitor, ncRepresenting the number of capacitors in parallel.
Figure BDA00017044618300000810
And
Figure BDA00017044618300000811
can be determined according to the actual environment of the power grid, for
Figure BDA00017044618300000812
And
Figure BDA00017044618300000813
the size of the present invention is not limited herein.
S102, determining a power flow equation corresponding to the power grid according to the objective function, the control variable and the constraint condition;
in this embodiment, the power flow equation in this step is determined according to the objective function, the control variable, and the constraint condition determined in the previous step, where the power flow equation corresponds to the control variable and has a corresponding state variable, and the state variable may be: active power of the generator at the slack node, voltage at a load node in the grid system, reactive power of the generator, and load on the transmission line.
In general, the expression of the trend equation minF (x, u) can be expressed by the following two equations:
min F(x,u)＝(f1(x,u),f2(x,u),...fi(x,u),...,fm(x,u))T
x∈Ωx,u∈Ωu,h(x,u)＝0,g(x,u)≤0
wherein x represents a control variable in the power grid system, u represents a state variable (i.e. a power grid configuration parameter to be solved) in the power grid system, m is the number of targets to be optimized (i.e. the number of objective functions), and fiFor the objective equation corresponding to the ith objective function, h (x, u) represents an equality constraint, g (x, u) represents an inequality constraint, and x ∈ ΩxExpressed is the control variable of the power grid, u ∈ ΩuRepresented is a set of state variables (grid configuration parameters) determined by x.
Based on the above-mentioned trend equation, applied to the present invention, the following equation can be used to represent:
minF(x1,u1)＝(minf1+minf2)
in the above formula, the variable x is controlled1Can be as follows: active power P of generator except for loose nodeGAnd terminal voltage V of generatorGGear T of transformer and susceptance Q of parallel capacitorcState variable (grid configuration parameter) u1Can be as follows: active power P of generator with loose nodeGVoltage V of load nodeLAnd reactive power Q of generatorGAnd a transmission load S on the transmission linel. Based on this, the variable x is controlled1And a state variable u1The following formula can be used for this:
Figure BDA0001704461830000091
wherein N isGNumber of generators, ntIs data of the transformer, ncIs the number of capacitors in parallel; at this time, the gear T of the transformer and the susceptance Q of the parallel capacitorcIs a discrete variable, but in the embodiment of the invention, the gear T of the transformer and the susceptance Q of the parallel capacitor are connectedcAs a continuous type variable.
Figure BDA0001704461830000092
Wherein NPQ is the number of load nodes, NlThe number of transmission lines.
It should be noted that, in this embodiment, other relevant information of the power flow equation is not described, and specific reference may be made to the prior art.
S103, solving the power flow equation by using a multi-target evolution algorithm based on decomposition to obtain the optimal power grid configuration parameters corresponding to the target function.
After the power flow equation is established, the power flow equation can be solved according to the objective function and each constraint condition. With regard to the solution, it is essential to determine the state variables in the power flow equation (i.e. the grid configuration parameters (active power P of the generator at the relaxation node)GVoltage V of load nodeLAnd reactive power Q of generatorGAnd a transmission load S on the transmission linel)). After a plurality of groups of state variables are obtained, the optimal state variable is selected as the optimal power grid configuration parameter in the embodiment of the invention. In this regard, the present invention will be described in detail in the following examples, which will not be described herein.
The method for controlling the multi-target optimal power flow for the power grid comprises the steps of firstly determining a target function and a constraint condition of the power grid, and then determining a power flow equation corresponding to the power grid according to the target function and the constraint condition; finally, solving a power flow equation by using a multi-target evolution algorithm based on decomposition to obtain an optimal power grid configuration parameter corresponding to a target function; wherein the objective function of the grid is the minimum generation cost of the generator and the minimum active power consumption of the transmission line. Therefore, the minimum power generation cost and the minimum active power loss are used as the objective function, so that the finally solved power grid configuration parameters can meet the condition that the power generation cost is minimum and the condition that the active power of a transmission line is minimum, and the power loss in the power grid system is reduced while the power generation cost in the power grid system is minimum.
Regarding the power flow equation corresponding to the power grid, the solution can be performed according to the following steps. The Gaussian Seidel method, the Newton Raphson method and the like can be adopted, and the multi-target evolution algorithm based on decomposition can obtain a group of non-dominated solutions (optimal power grid configuration parameters) when the power flow equation is optimized once, so that the multi-target optimization has inherent advantages, namely, the time efficiency of solving is high, and therefore, the multi-target evolution algorithm based on decomposition is adopted to solve the power flow equation.
Based on the above embodiment, as a preferred embodiment, step S103 includes:
the power flow equations are initialized to randomly generate a first solution set and a uniformly distributed set of weight vectors.
And iterating the solutions in the first solution set until an optimal solution corresponding to the objective function is obtained, wherein the optimal solution is an optimal power grid configuration parameter.
Specifically, in the present embodiment, in the pairAfter a power flow equation is initialized to obtain a first generation solution set (a first solution set), iteration is carried out on the first generation solution set until an optimal solution of a target function is obtained, at the moment, a new solution set is generated from a previous generation solution set (in a crossing and variation mode) during each iteration, then the new solution set and the previous generation solution set are merged, and target spatial clustering is carried out after merging. Specifically, the power flow equation is initialized according to each constraint condition in the power flow equation and an objective function and a control variable in the power flow equation (the initialization process can be referred to in the prior art), and the initialization is to randomly obtain a set of initial solutions (a first solution set) of the power flow equation, where the first solution set includes a plurality of sets of state variables (i.e., active power P of a generator with a plurality of sets of loose nodes)GVoltage V of load nodeLAnd reactive power Q of generatorGAnd a transmission load S on the transmission linelThe ratio of the parameters of) and, while obtaining the initial solution, also a set of evenly distributed weight vectors.
As a preferred embodiment, iterating each solution in the first solution set until obtaining an optimal solution corresponding to the objective function includes:
each solution in the first solution set is interleaved and mutated to obtain a second solution set.
And combining the first solution set and the second solution set to obtain a third solution set.
And carrying out target space clustering according to the weight vector set and the third solution set which are randomly generated during the initialization of the power flow equation until an optimal solution corresponding to the target function is obtained.
Specifically, in this embodiment, the crossover and the variance can simulate a two-level system crossover and a polynomial variance.
As a preferred embodiment, the performing target spatial clustering according to the weight vector set and the third solution set randomly generated during the initialization of the power flow direction includes:
normalizing each solution in the third solution set to obtain a target value corresponding to each solution in the third solution set;
calculating Euclidean distance between the target value and each weight vector in the weight vector set;
determining a target Euclidean distance and a target weight vector corresponding to the target Euclidean distance from the Euclidean distances;
assigning the target value to a cluster corresponding to the target weight vector;
and selecting a target solution from the cluster and distributing the target solution to a next generation solution set until an optimal solution corresponding to the target function is obtained.
In a most preferred embodiment, determining the target euclidean distance from the euclidean distances comprises:
and selecting the shortest Euclidean distance from the Euclidean distances as a target Euclidean distance.
Specifically, in this embodiment, in the optimal power flow problem, different objective functions may have different scales and units, so that each solution u needs to be solved firstxCarrying out normalization processing; for each solution u in the third solution set RxThe normalization process can be performed using the following equation:
Figure BDA0001704461830000111
wherein f isj'(ux) Expressing the solution u corresponding to the target equation of the jth target function after normalizationxThe target value of (2). f. ofj(ux) Express solution uxValue of the jth target equation, min fj(ux) Expressed is the solution uxIs allowed minimum value of jth target equation, max fj(ux) Expressed is the solution uxIs the maximum value of the allowed jth target equation. The target value for each solution in the third set may be calculated based on the above equation. After normalizing the target values in the third set, the solutions in the third set can be scaled to [0, 1%]Thereby reducing the amount of calculation in the subsequent operation. In this embodiment, the target function corresponds to two target equations, that is:
Figure BDA0001704461830000112
Figure BDA0001704461830000121
the target value for each solution in the third set can be calculated according to the above formulas.
Further, after obtaining the respective target values, the euclidean distance between each target value and each weight vector in the weight vector set (i.e. one target value corresponds to the same number of euclidean distances as the number of weight vectors in the weight vector set), where in this embodiment, the weight vectors are a set of uniform points distributed on a single standard line, and therefore, the distance from a corresponding target value to a certain weight vector r in the weight vector set may be defined as (f)1'(ux),...fm'(ux))TEuclidean distance from the intersection point of the standard simplex to r, Euclidean distance dist (u)xR) can be calculated using the following formula:
Figure BDA0001704461830000122
wherein r in the formulajRefers to the jth component of the weight vector r, and m is the number of components of the weight vector r. For the technical content not mentioned in the calculation of the euclidean distance, reference may be made to the prior art, and the present invention is not described herein again.
After the euclidean distance between each target value and each weight vector in the weight vector set is calculated, adding the target value to the cluster corresponding to the weight vector closest to the target value according to the magnitude of the euclidean distance for each target value in the set, wherein the cluster to which the target value is added can be represented by the following formula:
Figure BDA0001704461830000123
wherein R represents a third solution set, and C (R) represents a solution u corresponding to the target valuexAnd adding cluster, wherein RV is a weight vector set.
After putting the solution in the third set into the cluster, selecting a target solution from the cluster, wherein as a preferred embodiment, the selecting a target solution from the cluster comprises:
and determining the violation degree value of the solution in the cluster to the constraint condition.
And calculating the Chebyshev distance of the solutions in the cluster to the origin along the weight vector by using a standard quantization function.
Whether the violation degree satisfies a first condition and whether the Chebyshev distance satisfies a second condition are judged.
And taking the solution which simultaneously meets the first condition and the second condition as a target solution in the clustering cluster.
Specifically, in this embodiment, the violation degree value of the solution in the cluster to the constraint condition is: calculating the proportion of the devices in the cluster, the solutions of which violate one of the seven constraints mentioned in this embodiment, in the total number of the devices of the type of the power grid in this embodiment (of course, some of the seven constraints may also be used); taking the generator constraint as an example, if a certain solution in the cluster results in 4 generators not satisfying the constraint and there are 8 generators in the grid, the violation degree value is 0.5. Finally, the violation degree values of the constraint conditions in the embodiment of the present invention are obtained by adding the violation degree values of the solutions on the above 7 constraint conditions. The violation of the constraint is noted as: cv (x). As a preferred embodiment of the present invention, when the violation degree value cv (x) is [0,4], the violation degree value satisfies the first condition.
Further, in order to distinguish the quality of each solution in the cluster, the embodiment of the invention measures the quality of the solution by using a scalar function. Specifically, for the solution u in the cluster C (r)xCalculating the Chebyshev distance ASF (u) from the origin along the direction of the weight vector rxR) the following formula can be employed:
Figure BDA0001704461830000131
in general, ASF (u)xR) the smaller the function value, the higher the quality of the solution, but at this time it should be ensured that the solution only satisfies the constraint in the present embodiment, ASF (u)xAnd r) have a meaning. Therefore, for the weight vector r, it should be ensured that the ASF (u) can be used only when the following condition is satisfiedxAnd r) evaluating the solution:
first, the constraint violation degree value should be within a proper range, and the present embodiment is preferably [0,4]]An interval; next, for a solution in the cluster c (r), for a solution satisfying a second condition (partial order relationship of solutions in clusters) corresponding to the chebyshev distance in the solution set in the cluster: u. ofx1And ux2When u isx1And ux2Cv (x) at [0,4]]During the interval, judging when ux1And ux2Whether the following equation is satisfied:
0≤cv(ux1)≤cv(ux2)，ASF(ux1,r)≤ASF(ux2,r)
wherein, if the above relation is satisfied, the solution u in the cluster is calledx1Is superior to ux2And will ux1As solutions satisfying both the first and second conditions, i.e. ux1Is the target solution.
It should be noted that, when the solution set of each generation is iterated, the process of each iteration is the same process, and the iteration is not stopped until the optimal solution of the power flow equation. In addition, the solutions mentioned in the embodiments of the present invention are all solutions corresponding to the grid of the present invention, that is, the state variable corresponding to the control variable (active power P of the generator relaxing the node)GVoltage V of load nodeLAnd reactive power Q of generatorGAnd a transmission load S on the transmission linel) The optimal solution solved by the scheme of the embodiment of the invention is the active power P of the generator of the relaxation node in the power gridGVoltage V of load nodeLAnd reactive power Q of generatorGAnd a transmission load S on the transmission linelAnd (4) optimal power grid configuration parameters.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a multi-target optimal power flow control device for a power grid according to an embodiment of the present invention, including:
a determining module 201, configured to determine an objective function, a constraint condition, and a control variable of a power grid;
the power flow equation determining module 202 is used for determining a power flow equation corresponding to the power grid according to the target function, the control variable and the constraint condition;
the solving module 203 is used for solving the power flow equation by using a multi-target evolution algorithm based on decomposition to obtain an optimal power grid configuration parameter corresponding to the target function;
wherein the objective function of the power grid comprises a minimum power generation cost of the generator and a minimum active power loss of the transmission line.
The invention discloses a multi-target optimal power flow control device for a power grid, which comprises the steps of firstly determining a target function and a constraint condition of the power grid, and then determining a power flow equation corresponding to the power grid according to the target function and the constraint condition; finally, solving a power flow equation by using a multi-target evolution algorithm based on decomposition to obtain an optimal power grid configuration parameter corresponding to a target function; wherein the objective function of the grid is the minimum generation cost of the generator and the minimum active power consumption of the transmission line. Therefore, the minimum power generation cost and the minimum active power loss are used as the objective function, so that the finally solved power grid configuration parameters can meet the condition that the power generation cost is minimum and the condition that the active power of a transmission line is minimum, and the power loss in the power grid system is reduced while the power generation cost in the power grid system is minimum.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a multi-target optimal power flow control device for a power grid according to an embodiment of the present invention, including:
a memory 301 for storing a computer program;
a processor 302 for executing the computer program stored in the memory to implement the steps of the multi-objective optimal power flow control method for an electrical grid as mentioned in any of the above embodiments.
It should be noted that the multi-objective optimal power flow control device for a power grid disclosed in this embodiment has the same technical effect as the technical solution mentioned in any of the above embodiments, and here, the embodiments of the present invention are not described in detail again.
The method, the device and the equipment for controlling the multi-target optimal power flow of the power grid disclosed by the application are introduced in detail. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

Claims (7)

1. A multi-target optimal power flow control method for a power grid is characterized by comprising the following steps:
determining an objective function, a constraint condition and a control variable of a power grid;
determining a power flow equation corresponding to the power grid according to the target function, the control variable and the constraint condition;
solving the power flow equation by using a multi-target evolution algorithm based on decomposition to obtain an optimal power grid configuration parameter corresponding to the target function;
wherein the objective function of the power grid comprises a minimum power generation cost of the generator and a minimum active power loss of the transmission line;
solving the power flow equation by using a multi-target evolution algorithm based on decomposition to obtain the optimal power grid configuration parameter corresponding to the target function comprises the following steps:
initializing the power flow equation to randomly generate a first solution set;
iterating each solution in the first solution set until an optimal solution corresponding to the objective function is obtained, wherein the optimal solution is the optimal power grid configuration parameter;
the iterating each solution in the first solution set until an optimal solution corresponding to the objective function is obtained includes:
crossing and mutating each solution in the first solution set to obtain a second solution set;
merging the first solution set and the second solution set to obtain a third solution set;
performing target space clustering according to the weight vector set and the third solution set which are randomly generated during the initialization of the power flow equation until an optimal solution corresponding to the target function is obtained;
the target space clustering according to the weight vector set randomly generated during the initialization of the power flow equation and the third solution set comprises:
normalizing each solution in the third set of solutions to obtain a target value corresponding to each solution in the third set of solutions;
calculating a Euclidean distance between the target value and each weight vector in the weight vector set;
determining a target Euclidean distance and a target weight vector corresponding to the target Euclidean distance from each Euclidean distance;
assigning the target value to a cluster corresponding to the target weight vector;
and selecting a target solution from the clustering cluster and distributing the target solution to a next generation solution set until an optimal solution corresponding to the target function is obtained.
2. The method of claim 1, wherein the determining the objective function, constraints, and control variables of the power grid comprises:
taking the active power of the generator and the terminal voltage of the generator as the control variables;
determining a cost coefficient of the generator and a conductance of the transmission line;
determining a minimum power generation cost of the generator using the active power of the generator and a cost coefficient of the generator;
determining the minimum active power loss of the transmission line by using the voltage of the load nodes at two ends of the transmission line and the conductance of the transmission line;
determining that a terminal voltage of the generator is between a minimum operating voltage of the generator and a maximum operating voltage of the generator as a first constraint condition;
determining that the active power of the generator is in a second constraint condition between the minimum active power of the generator and the maximum active power of the generator;
determining that the reactive power of the generator is between the minimum reactive power of the generator and the maximum reactive power of the generator as a third constraint condition;
determining that the voltage of the load node on the transmission line is between the minimum load voltage and the maximum load voltage as a fourth constraint condition;
determining that the load of the transmission line does not exceed a maximum load amount is a fifth constraint.
3. The multi-objective optimal power flow control method for an electrical grid according to claim 2, wherein the control variables further comprise: a transformer tap of the grid, a susceptance of a parallel capacitor of the grid;
correspondingly, the constraint condition further includes: a sixth constraint condition that the transformer gear of the power grid is between the minimum transformer gear and the maximum transformer gear is set;
it is a seventh constraint that the reactive power of the parallel capacitor is between the minimum reactive power of the parallel capacitor and the maximum reactive power of the parallel capacitor.
4. The method of claim 1, wherein the determining a target euclidean distance from each of the euclidean distances comprises:
and selecting the shortest Euclidean distance from the Euclidean distances as the target Euclidean distance.
5. The multi-objective optimal power flow control method for an electrical grid according to claim 1, wherein the selecting a target solution from the cluster comprises:
determining a violation degree value of a solution in the cluster to the constraint condition;
calculating Chebyshev distances of solutions in the cluster to an origin along the target weight vector by using a scalar function;
judging whether the violation range value meets a first condition and whether the Chebyshev distance meets a second condition;
and taking the solution which simultaneously meets the first condition and the second condition as a target solution in the clustering cluster.
6. A multi-objective optimal power flow control apparatus for an electrical grid, comprising:
the determining module is used for determining an objective function, a constraint condition and a control variable of the power grid;
the power flow equation determining module is used for determining a power flow equation corresponding to the power grid according to the target function, the control variable and the constraint condition;
the solving module is used for solving the power flow equation by using a multi-target evolution algorithm based on decomposition to obtain an optimal power grid configuration parameter corresponding to the target function;
wherein the objective function of the power grid comprises a minimum power generation cost of the generator and a minimum active power loss of the transmission line;
solving the power flow equation by using a multi-target evolution algorithm based on decomposition to obtain the optimal power grid configuration parameter corresponding to the target function comprises the following steps:
initializing the power flow equation to randomly generate a first solution set;
iterating each solution in the first solution set until an optimal solution corresponding to the objective function is obtained, wherein the optimal solution is the optimal power grid configuration parameter;
the iterating each solution in the first solution set until an optimal solution corresponding to the objective function is obtained includes:
crossing and mutating each solution in the first solution set to obtain a second solution set;
merging the first solution set and the second solution set to obtain a third solution set;
performing target space clustering according to the weight vector set and the third solution set which are randomly generated during the initialization of the power flow equation until an optimal solution corresponding to the target function is obtained;
the target space clustering according to the weight vector set randomly generated during the initialization of the power flow equation and the third solution set comprises:
normalizing each solution in the third set of solutions to obtain a target value corresponding to each solution in the third set of solutions;
calculating a Euclidean distance between the target value and each weight vector in the weight vector set;
determining a target Euclidean distance and a target weight vector corresponding to the target Euclidean distance from each Euclidean distance;
assigning the target value to a cluster corresponding to the target weight vector;
and selecting a target solution from the clustering cluster and distributing the target solution to a next generation solution set until an optimal solution corresponding to the target function is obtained.
7. A multi-objective optimal power flow control apparatus for an electrical grid, comprising:
a memory for storing a computer program;
a processor for executing the computer program stored in the memory to implement the steps of the multi-objective optimal power flow control method for an electric grid according to any one of claims 1 to 5.
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