A power flow control method for power transmission section based on genetic algorithm

Abstract

The present invention relates to the field of power transmission sections of power grids, and provides a power transmission section flow control method based on a genetic algorithm. The method includes the following steps: setting the unit that controls the power transmission section according to the sensitivity ranking of all generator sets to the power transmission of the power transmission section; performing algorithm encoding on the set power transmission section control unit, and querying the optimal solution with the evolution termination condition; and outputting the adjustment result of the generator set according to the set power transmission section flow adjustment target. The present invention can greatly improve the efficiency and reduce the application cost by selecting the control unit through sensitivity ranking. In solving more complex combinatorial optimization problems, better optimization results can be obtained faster through genetic algorithms. The genetic algorithm itself can also adopt dynamic adaptive technology to automatically adjust the algorithm control parameters and coding accuracy during the evolution process. The overall search strategy and optimization search method of the genetic algorithm have strong robustness in calculation.

Description

Technical Field

[0001] The invention relates to the field of power grid transmission sections, and in particular to a power grid transmission section power flow control method based on a genetic algorithm. Background Art

[0002] my country's power grid is interconnected between multiple regional power grids to form a large interconnected power grid; the interconnected lines between different load centers constitute transmission channels. These transmission channels have a decisive impact on the safety and stability of the entire power grid and are the key to maintaining the safe and stable operation of the entire system. Therefore, in the process of power system research and analysis, the transmission section is taken as an important focus for research and analysis.

[0003] The dispatchers of the power dispatching center rely on various information and functions provided by the power dispatching automation system to carry out daily dispatching of the power system. Among them, monitoring and controlling the stable section of the power grid is an important task. However, the existing power dispatching automation system provides relatively limited technical support for dispatching operations, which is reflected in the fact that the stable section of the power grid currently only has a monitoring function, and the control of the stable section flow can only be done manually. With the continuous expansion of the scale of the power grid, the increasing complexity of the power grid structure and the complexity and changeability of the power grid operation mode, it has become increasingly difficult to control the stable section of the power grid manually. In order to solve this technical problem, a power grid transmission section flow control method based on genetic algorithm is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a power grid transmission section flow control method based on genetic algorithm to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A power transmission section power flow control method based on genetic algorithm comprises the following steps;

[0007] According to the sensitivity ranking of all generator sets to the transmission power of the transmission section, the generator set controlling the transmission section is set;

[0008] Encode the algorithm for the set transmission section control unit and query the optimal solution with the evolution termination condition;

[0009] According to the set transmission section flow adjustment target, the generator set adjustment result is output.

[0010] As a further solution of the present invention: the setting of the transmission section control unit includes the following steps:

[0011] The sensitivity of the transmission power of the transmission section is calculated based on the power flow equation; the sensitivity data of the output of all the calculated generator units are sorted, and the control unit is selected according to the sorting.

[0012] As a further solution of the present invention: a method for selecting control units according to ranking includes: selecting the first N units from multiple groups of rankings as the units for controlling the transmission section.

[0013] As a further solution of the present invention: the algorithm encoding of the control unit includes the following steps:

[0014] According to the control variables involved in the optimal control of the power flow of the power transmission section of the power grid;

[0015] Obtaining a fitness function according to a crossover operation in a genetic algorithm or according to a mutation operation;

[0016] Based on the niche technology, the genetic algorithm selection operation is carried out to select the global optimal solution from the local optimal solution;

[0017] Use uniform crossover to perform crossover operation on the chromosomes of the previous generation to obtain the crossover probability;

[0018] The total population of the previous and next generations generated after the crossover operation are arranged, and the individuals in the chromosomes of the next generation are replicated based on the Metropolis discriminant criterion;

[0019] The mutation rate is set, the chromosomes of the replication operation are mutated, the local optimal loop iteration strategy is combined with the maximum genetic generation number as the evolution termination condition, and the loop iteration is repeated to find the optimal solution.

[0020] As a further solution of the present invention: in the coding strategy, the control variables involved in the power flow optimization control of the power grid transmission section include continuous variables and discrete variables, binary coding is used for discrete variables, and floating point coding is used for continuous variables.

[0021] As a further solution of the present invention: the replication operation based on the Metropolis discriminant criterion is: a crossover operation is performed on the chromosomes of the previous generation in a uniform crossover manner, and after the crossover operation, twice the total population number of the previous generation and the next generation is generated. When the generated total population number of the previous generation and the next generation is arranged, the chromosomes of the previous generation are arranged before the chromosomes of the next generation, and the total population number of the chromosomes of the next generation is reduced by one individual by the Metropolis discriminant criterion replication operation.

[0022] As a further solution of the present invention: the evolution termination condition is to combine the local optimal loop iteration strategy with the maximum genetic generation number.

[0023] As a further solution of the present invention: the method for controlling the power transmission section flow comprises the following steps:

[0024] Set the target of transmission section power flow adjustment; output the result of generator set adjustment.

[0025] As a further solution of the present invention: the target of setting the power flow adjustment of the transmission section is specifically: setting a target, that is, setting the transmission power on the transmission section, and setting a smaller threshold.

[0026] As a further solution of the present invention: the result of the output generator set adjustment is formed by inputting the original data and genetic algorithm parameters to form a chromosome matrix; through the genetic algorithm encoding step, until the transmission section adjustment reaches the set value threshold, and then multiple iterations are performed to make the output value infinitely close to the set value, and finally the adjustment result of the generator set adjustment is output.

[0027] As a further solution of the present invention: when controlling the power transmission section flow, the following steps are also included: inputting original data and genetic algorithm parameters to form a chromosome matrix and an admittance matrix, initializing the population, modifying the admittance matrix, performing flow calculation control, calculating the fitness function, updating individual records, and judging the termination conditions after selecting operations, crossover operations, copying operations, and compiling operations. After the evolution termination conditions are met, the adjustment result of the generator set is output, otherwise the cycle iteration is repeated until the adjustment result of the generator set is output.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. Selecting control units by sensitivity ranking can greatly improve efficiency while reducing application costs.

[0030] 2. When solving more complex combinatorial optimization problems, such as transmission section flow calculation, genetic algorithms can obtain better optimization results more quickly.

[0031] 3. The genetic algorithm itself can also use dynamic adaptive technology to automatically adjust the algorithm control parameters and coding accuracy during the evolution process.

[0032] 4. The overall search strategy and optimization search method of the genetic algorithm do not rely on gradient information or other auxiliary knowledge during calculation and are highly robust. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of the power transmission section flow control method of the present invention based on genetic algorithm.

[0034] Figure 2 The present invention is a schematic diagram of the operation flow of a power grid transmission section power flow control method based on a genetic algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] The disclosure below provides many different embodiments or examples to realize different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplicity and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides various specific examples of processes and materials, but those of ordinary skill in the art can be aware of the application of other processes and / or the use of other materials.

[0037] refer to Figure 1 This is a flow chart of a power transmission section flow control method based on genetic algorithm.

[0038] See also Figure 1 , a power transmission section flow control method based on genetic algorithm provided by the present invention comprises the following steps S1-S3;

[0039] S1. According to the sensitivity ranking of all generator sets to the transmission power of the transmission section, set the generator set to control the transmission section;

[0040] S2. Algorithm coding is performed on the set transmission section control unit, and the optimal solution is searched based on the evolution termination condition;

[0041] S3. Output the adjustment result of the generator set according to the set transmission section power flow adjustment target.

[0042] The invention discloses a method for controlling power transmission section flow of a power grid based on a genetic algorithm. By selecting control units by sensitivity sorting, the efficiency can be greatly improved while reducing application costs. When solving more complex combinatorial optimization problems, such as calculation of power transmission section flow, better optimization results can be obtained more quickly through genetic algorithms. The genetic algorithm itself can also adopt dynamic adaptive technology to automatically adjust the algorithm control parameters and coding accuracy during the evolution process. The overall search strategy and optimization search method of the genetic algorithm do not rely on gradient information or other auxiliary knowledge during calculation, and have strong robustness.

[0043] The following will be combined with the drawings in the exemplary embodiments of the present invention to clearly and completely describe the technical solutions in the exemplary embodiments of the present invention. Obviously, the exemplary embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a method for controlling power flow in a power grid transmission section based on a genetic algorithm, comprising the following steps:

[0045] Step 1: Setting of transmission section control unit.

[0046] In this embodiment, the setting of the transmission section control unit includes calculating the sensitivity of the transmission power of the transmission section based on the power flow equation; sorting the sensitivity data of the output of all the calculated generator units, and selecting the control unit according to the sorting. For details, please refer to the following steps:

[0047] Step 1.1: Calculate the sensitivity of all generators to the power delivered by the transmission section.

[0048] In this embodiment, the calculation of the output of all generator sets is to calculate each power flow, and the calculation of the sensitivity of the transmission power of the transmission section is also based on the power flow equation. The power flow equation is:

[0049] F(X,U,α)=0 (1)

[0050] Y=G(X,U,α) (2)

[0051] In the formula, α is a parameter variable, such as system component parameters G, B, etc.; X is a state variable, including unknown quantities in the power flow equation, such as the voltage amplitude and phase angle of the PQ node; U is a control variable, including all node known quantities, such as the active and reactive power of the load node, etc.; Y is an output variable, that is, the line power flow P, Q, etc.

[0052] The control variables and parameter variables of the above power flow equations (1) and (2) are derived respectively:

[0053]

[0054]

[0055]

[0056]

[0057] Due to the mathematical consistency of formulas (3) to (6) with the modified equations in the iterative process of the Newton power flow formula, after the power flow converges, it is only necessary to determine the sensitivity control variable required. Then, the sensitivity index of the output of all generator sets can be obtained by back-substitution calculation of the power flow iteration. Then, based on the polar coordinate AC power flow calculation model, the sensitivity of the power flow of each branch L to the power injection of node α is calculated, and the derivation formula is as follows:

[0058]

[0059] Where P ii is the active power transmitted by the line between nodes i and j; P α is the active power injected into node α; V and θ are the voltage amplitude and phase angle respectively; and It can be obtained according to formula (3) to (6); and are functions of the node voltage V and θ at both ends of the branch, respectively, and can be obtained based on power flow calculation:

[0060]

[0061]

[0062] Where: kj is the voltage phase difference between node k and node j; G kj and B kj are the conductance and susceptance between node k and node j respectively, and N is the total number of system nodes.

[0063] Step 1.2: Sort the units by sensitivity.

[0064] By calculating the sensitivity of all generator sets, we obtain the sensitivity data S1, S2, ... S N Among them, there are sensitivities that positively affect the transmission power of the transmission section, that is, when the output of the generator set is reduced, the transmission power decreases; there are also sensitivities that negatively affect the transmission power of the transmission section, that is, when the output of the generator set is reduced, the transmission power increases. The positive and negative sensitivity data are arranged in order from large to small.

[0065] Step 1.3: Select the control unit by sorting

[0066] Based on the rankings obtained in the above steps 1.1 and 1.2, the first 10 units are selected from each of the two rankings as the units controlling the transmission section.

[0067] Step 2: Encode the algorithm for the control unit.

[0068] In this embodiment, since the genetic algorithm cannot directly process the parameters of the problem space, the problem to be solved must be represented as a chromosome or individual in the genetic space through encoding.

[0069] In this embodiment, the algorithm encoding of the control unit includes the following steps:

[0070] According to the control variables involved in the optimal control of the power flow of the power transmission section of the power grid;

[0071] Obtaining a fitness function according to a crossover operation in a genetic algorithm or according to a mutation operation;

[0072] Based on the niche technology, the genetic algorithm selection operation is carried out to select the global optimal solution from the local optimal solution;

[0073] Use uniform crossover to perform crossover operation on the chromosomes of the previous generation to obtain the crossover probability;

[0074] The total population of the previous and next generations generated after the crossover operation are arranged, and the individuals in the chromosomes of the next generation are replicated based on the Metropolis discriminant criterion;

[0075] The mutation rate is set, the chromosomes of the replication operation are mutated, the local optimal loop iteration strategy is combined with the maximum genetic generation number as the evolution termination condition, and the loop iteration is repeated to find the optimal solution.

[0076] In this embodiment, algorithm coding of the control unit specifically includes the following steps:

[0077] Step 2.1: Coding strategy.

[0078] The control variables involved in the power flow optimization control of the power transmission section include continuous variables and discrete variables. For example, the gear adjustment of the transformer tap and the number of reactive compensation devices switched on and off for the control unit are discrete variables; while the generator terminal voltage, DC voltage, DC current, etc. are continuous variables. When encoding, a mixed encoding method is required, that is, binary encoding is used for discrete variables and floating-point encoding is used for continuous variables.

[0079] According to the control variables involved in the power flow optimization control of the power transmission section of the power grid, the generator terminal voltage U G , reactive power compensation capacity Q C , Transformer ratio T K , DC voltage U dc and current I d Etc. are respectively represented as chromosomes of genetic algorithm, namely:

[0080] X=[U G Q C TK U dc P dc Q dc ] (10)

[0081] The chromosome formula for expressing the discrete variables in the genetic algorithm is:

[0082] T Ki =T Kimin +D TKi S TKi (11)

[0083] Q Ci =D QCi S QCi (12)

[0084] Where T Kimin is the minimum transformation ratio of the transformer; D TKi is the number of transformer gears; S TKi is the step length of each gear of the transformer; D QCi S is the number of reactive power compensation device switching groups; QCi It is the step length of reactive power compensation switching amount.

[0085] Step 2.2: Choose a fitness function.

[0086] Usually the fitness function is closely related to the objective function in the power flow control optimization model. The fitness function can be obtained according to the crossover operation in the genetic algorithm:

[0087]

[0088] Where C max is the maximum value of F(x).

[0089] According to the mutation operation, the fitness function can be obtained:

[0090]

[0091] Step 2.3: Selection operation based on niche technology.

[0092] The selection operation is the key to determine the evolution direction in the genetic algorithm. In order to improve the possibility of finding the global optimal solution for tidal control, the microhabitat technology is used for the selection operation of the genetic algorithm. This method is mainly based on the fact that organisms generally live together with the same species as themselves during the evolution process, and the offspring of co-reproduction also live in a specific area. Through the microhabitat technology, the global optimal solution can be found among more local optimal solutions.

[0093] Calculate the Euclidean distance between all pairs of individuals:

[0094]

[0095] Where: M N is the length of the individual; X i , X j are the i-th and j-th individuals respectively; X ik , X jk is the individual coordinate. The size of the Euclidean distance is used to determine whether individuals need to be eliminated, thereby ensuring the evolution of the group.

[0096] Step 2.4: Crossover operation.

[0097] The crossover operation is an operation that crosses the chromosome genes of the previous generation so that the next generation chromosome has multiple genes of the previous generation. It is the key to ensure the diversity of population evolution. The crossover operation is performed on the chromosomes of the previous generation using uniform crossover, and the crossover probability is:

[0098]

[0099] Where: f max is the maximum fitness value in the group; f avg is the average fitness of the group; P c1 and P c2 are the upper and lower limits of the crossover probability.

[0100] Step 2.5: Copy operation based on Metropolis criterion.

[0101] Assume that the number of all previous generation chromosomes in the population is N N , then the total number of populations of the previous and next generations generated after the crossover operation is 2N N , and arrange the chromosomes of the previous generation in the first N N The chromosomes of the next generation are arranged in the Nth N Then the next generation chromosome N is replicated using the Metropolis discrimination criterion. N -1 individual, its probability function is:

[0102]

[0103]

[0104] Where: f(x i )、f(x j ) are individual x i 、x j T is the temperature value.

[0105] Step 2.6: Mutation operation.

[0106] The mutation operation is to increase the natural random diversity of chromosomes. By considering the coexistence of discrete variables and continuous variables in the power flow optimization control variables, the mutation rate is set:

[0107]

[0108] n2 is the number of chromosomes; f min is the minimum fitness value in the group.

[0109] Step 2.7: Evolution termination condition.

[0110] Traditional genetic algorithms usually only use genetic algebra as the evolution termination condition, which can easily result in the final optimization result being only the local optimal solution. By combining the local optimal loop iteration strategy with the maximum genetic algebra as the evolution termination condition, when the evolution iteration has met the convergence condition, the probability value of the population crossover operation and mutation operation is increased by repeated loop iterations, and the optimal solution is continued to be found to ensure the global optimality of the optimization result.

[0111] Step 3: Transmission section flow control.

[0112] In the embodiments of the present invention, see Figure 2 As shown, the power transmission section flow control includes the following steps: inputting original data and genetic algorithm parameters, forming a chromosome matrix and an admittance matrix, initializing the population, modifying the admittance matrix, performing flow calculation control, and calculating the fitness function, updating individual records, and performing termination condition judgment after selection operations, crossover operations, copy operations, and compilation operations. After the evolution termination conditions are met, the adjustment result of the generator set adjustment is output, otherwise the cycle iteration is repeated until the adjustment result of the generator set adjustment is output.

[0113] In this embodiment, the power transmission section flow control includes the following steps:

[0114] Step 3.1: Set the target for transmission section power flow adjustment.

[0115] Set a target, that is, set the transmission power on the transmission section and set a smaller threshold.

[0116] Step 3.2: Output the results of generator set adjustment.

[0117] By inputting the original data and genetic algorithm parameters, a chromosome matrix is ​​formed; through the genetic algorithm encoding steps, the transmission section adjustment is carried out until it reaches the set value threshold, and multiple iterations are performed to make the output value infinitely close to the set value, and finally the adjustment result of the generator set is output.

[0118] In summary, the power grid transmission section flow control method based on genetic algorithm of the present invention can greatly improve efficiency and reduce application cost by selecting control units by sensitivity sorting; when solving more complex combinatorial optimization problems, such as transmission section flow calculation, better optimization results can be obtained more quickly through genetic algorithms; the genetic algorithm itself can also adopt dynamic adaptive technology to automatically adjust algorithm control parameters and coding accuracy during the evolution process; the overall search strategy and optimization search method of the genetic algorithm do not rely on gradient information or other auxiliary knowledge during calculation, and have strong robustness.

[0119] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0120] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for controlling power flow in power transmission section based on genetic algorithm, characterized in that: The method comprises the following steps: According to the sensitivity ranking of all generator sets to the transmission power of the transmission section, the generator set controlling the transmission section is set; Encode the algorithm for the set transmission section control unit and query the optimal solution with the evolution termination condition; According to the set transmission section power flow adjustment target, output the adjustment result of the generator set; The setting of the transmission section control unit includes the following steps: Calculate the sensitivity of power transmission at the transmission section based on the power flow equation; sort the sensitivity data of all calculated generator outputs, and select the control unit according to the sorting; The method of selecting the control unit according to the ranking includes: selecting the first N units from each of the multiple ranking groups as the units for controlling the transmission section; The algorithm coding of the control unit includes the following steps: According to the control variables involved in the optimal control of the power flow of the power transmission section of the power grid; Obtaining a fitness function according to a crossover operation in a genetic algorithm or according to a mutation operation; Based on the niche technology, the genetic algorithm selection operation is carried out to select the global optimal solution from the local optimal solution; Use uniform crossover to perform crossover operation on the chromosomes of the previous generation to obtain the crossover probability; The total population of the previous and next generations generated after the crossover operation are arranged, and the individuals in the chromosomes of the next generation are replicated based on the Metropolis discriminant criterion; The mutation rate is set, the chromosomes of the replication operation are mutated, the local optimal loop iteration strategy is combined with the maximum genetic generation number as the evolution termination condition, and the loop iteration is repeated to find the optimal solution.

2. The method for controlling power transmission section flow of a power grid based on genetic algorithm according to claim 1, characterized in that: In the coding strategy, the control variables involved in the power flow optimization control of the power transmission section of the power grid include continuous variables and discrete variables. Binary coding is used for discrete variables, and floating point coding is used for continuous variables.

3. The method for controlling power transmission section flow of a power grid based on genetic algorithm according to claim 1, characterized in that: The replication operation based on the Metropolis discriminant criterion is: a crossover operation is performed on the chromosomes of the previous generation in a uniform crossover manner, and after the crossover operation, twice the total population number of the previous generation and the next generation is generated. When the total population numbers of the previous generation and the next generation are arranged, the chromosomes of the previous generation are arranged before the chromosomes of the next generation. The total population number of the chromosomes of the next generation is reduced by one individual by the replication operation using the Metropolis discriminant criterion.

4. The method for controlling power transmission section flow of a power grid based on genetic algorithm according to claim 1, characterized in that: The evolution termination condition is to combine the local optimal loop iteration strategy with the maximum genetic generation number.

5. The method for controlling power transmission section flow of a power grid based on genetic algorithm according to claim 1, characterized in that: The method for controlling power flow at a transmission section comprises the following steps: The target of power flow adjustment of the transmission section is set; the result of the adjustment of the generator set is output, wherein the target of the adjustment is the set target, the set target is the transmission power on the set transmission section, and a smaller threshold is set.

6. The method for controlling power transmission section flow of a power grid based on genetic algorithm according to claim 5, characterized in that: The output generator set adjustment result is formed by inputting original data and genetic algorithm parameters to form a chromosome matrix; through the genetic algorithm encoding step, until the transmission section adjustment reaches the set value threshold, and then multiple iterations are performed to make the output value infinitely close to the set value, and finally the adjustment result of the generator set adjustment is output.

7. The method for controlling power flow in power transmission section based on genetic algorithm according to claim 5, characterized in that: When controlling the power flow of a transmission section, the following steps are also included: inputting original data and genetic algorithm parameters, forming a chromosome matrix and an admittance matrix, initializing the population, modifying the admittance matrix, performing power flow calculation control, calculating the fitness function, updating individual records, judging the termination conditions after selecting operations, crossover operations, copying operations, and compiling operations, and outputting the adjustment results of the generator set adjustment after the evolution termination conditions are met, otherwise repeating the loop iteration until the adjustment results of the generator set adjustment are output.

Images