JPH0833207A - Reactive power planning method for power system - Google Patents

Abstract

PURPOSE:To realize reactive power control closer to an optimal solution by generating a chromosome in a genetic algorithm employing genes as control variables, performing a genetic operation for the chromosome and evaluating the genetic operation using a total objective expression. CONSTITUTION:(k) normalized values Pnl1-Pnlk corresponding to solids P1, (p) normalized values Pnn1-Pnnp corresponding to solids Pn, and the like, are encoded and coupled randomly with corresponding rows thus generating individuals having chromosomes 1-i. The chromosomes are then subjected to genetic operations including selection, crossing, and mutation and power flow is analyzed using control variables determined through the genetic operation. In other words, calculation is made for an objective expression related to the total effective power loss and total voltage variation. The genetic operation and evaluation are repeated until a significant variation disappears from the objective expression and all chromosomes are settled.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for preparing a reactive power plan of a power system for appropriately distributing reactive power in the power system and maintaining the system voltage at a proper value.

[0002]

2. Description of the Related Art In a power system, a reactive power plan is established in accordance with a demand for reactive power in a node or a load bus in the system. Loads connected to the system typically consume or generate reactive power. The electric power system has a characteristic that the voltage drops when the reactive power is consumed and rises when the reactive power is generated. Therefore, the reactive power must be appropriately controlled in order to maintain the voltage of the power system at the specified value. Controls include power capacitors, shunt reactors, transformer taps, and generators that are pre-installed in the power grid. When the power capacitor is turned on, reactive power is generated and the voltage rises. Conversely, when the shunt reactor is turned on, reactive power is consumed and the voltage drops. For this reason, in order to maintain an appropriate voltage in the power system, a reactive power distribution plan, and, for example, predicting reactive power consumption in the next year and installing equipment such as power capacitors and shunt reactors to match it There is a need for a method for planning the installation of phase-modifying equipment.

Reactive power is controlled by a generator, a synchronous phaser, a switchable capacitor bank and a tap change transformer. The shunt reactor also absorbs excess reactive power in the system. When changes occur in the system, the reactive power control equipment is controlled in such a way that the behavior of the system is optimized. That is, the reactive power optimization is being executed. When the load and the configuration in the system change drastically with time, the reactive power optimization process needs to be performed sufficiently fast according to the change.

The reactive power plan is mainly designed so that the total cost of the system is minimized, the transmission loss is minimized, and the voltage fluctuation is maintained within a specified range. Optimal power flow calculations are widely used for reactive power planning. Optimal power flow calculations generally deal with both active and reactive power optimization. Optimization of active power, known as Economic Load Distribution (ELD), is to optimize the power generation schedule of each generator taking into account system constraints. On the other hand, the optimization of reactive power is
Within each specification range, control bus voltage, transformer tap settings, power capacitors, shunt reactors, reactive power from generators, etc. to minimize transmission losses.

In a large-scale power system, reactive power is supplied to a large number of inductive loads from a reactive power source including a load tap switching transformer. A proper reactive power plan can reduce the amount of reactive power flowing in the power grid, thereby reducing voltage fluctuations and transmission losses in the power grid. In the following, the power system may be referred to as a system, and the transmission loss may be referred to as a loss.

Institute of Electrical and Electronics Engineers Computer Applications (IEEE Computer Applicati
ons in Power), July 1988, pp. 16-21.
As described on page, there are two different approaches to reactive power optimization processing. One of them is a method of processing the system by linear approximation. That is, the system constraint condition and the objective function are expressed as linear ones. This method is suitable for systems with low fluctuations.
In addition, the method uses linear programming that optimizes the linear problem, and power flow calculation to confirm the effect of the distribution of reactive power. That is, the simulation by the linear programming algorithm and the power flow calculation is repeated until the objective function becomes optimum.

Another approach is a method of linearly approximating the constraint conditions and expressing the objective function in a non-linear manner. That is, it is a non-linear technique using a linear constraint condition. Various methods using this technique have been proposed. For example, there is a method that combines a conjugate gradient method and a direct search method that minimizes loss. Alternatively, there is a least squares method that minimizes system loss. The above two approaches can be applied to control the deviation from the optimum voltage set value determined from the optimum power flow offline in a relatively small power system. However, it does not cover the entire range of system changes that can actually occur. Also,
Not applicable online due to long calculation time.

In large-scale system reactive power planning,
The problem of reactive power optimization of the entire system is formulated by an equality and inequality constraint condition and an objective function that reflects one of the behaviors of the system. Some variables on each bus are known. And the value of the unknown variable is
It is obtained by performing system optimization so as to minimize the value of the objective function within the constraints. Here, since the constraint condition and the objective function are non-linear, the solution method of this problem depends on the non-linear programming method.

A formulation for reactive power planning of a large-scale power system will be described below. The objective function, that is, the transmission loss is expressed as follows. In _{P L = (V i -V j} ) 2 G ij ··· (1) where, V _i, V _j, respectively, i th, a voltage of the j-th node. Further, G _ij is R _ij / (R _ij ² + X
_ij ² ) is the conductance of the line between the nodes i and j. Note that R _ij is a resistance component and X _ij is a reactance component.

The constraint condition (equation) is expressed as follows. Σ _{j = 1} ^NQ Q _Gi −Q _D −Q _L = 0 (2) Here, Q _Gi is the total generated reactive power in the j-th bus, Q _D is the total required reactive power of the system, and Q _L Is the total reactive power loss of the system and NQ is the total number of buses with reactive power sources. There are also the following constraints (inequality). First, P _Gj ^min ≤ P _Gj ≤ P _Gj ^max (3). Here, P _Gj ^min is the lower limit of the active power generated by each generator, and P _Gj ^max is the upper limit of the active power generated by each generator. That is, this constraint defines the range of active power of each generator. Further, V _j ^min ≤V _j ≤V _j ^max (4). Here, V _j ^min and V _j ^max are the lower and upper limits of each bus voltage. That is, this constraint defines the range of bus voltage.

Further, there are the following restrictions regarding control variables. First of all, it is a ^{_{Q Gj min ≦ Q Gj ≦ Q}} Gj max ··· (5). Here, Q _Gj ^min and Q _Gj ^max are the lower limit and the upper limit of the reactive power generated by the reactive power source. That is, this constraint defines the range of the reactive power of the reactive power source. Further, a ^{_{V Gj min ≦ V Gj ≦ V}} Gj max ··· (6). Here, V _Gj ^min and V _Gj ^max are the upper and lower limits of the terminal voltage of each generator. That is, this constraint is
It specifies the range of the terminal voltage of the generator. Further, a ^{_{T pj min ≦ T pj ≦ T}} pj max ··· (7). Here, T _pj ^min and T _pj ^max are the upper limit and the lower limit of the tap of each transformer. That is, this constraint defines the tap position of the transformer.

The distribution control of the reactive power in the system is realized by adjusting the following control variables. 1. Transformer tap position 2. Generator voltage 3. Turning on and off power capacitors and shunt reactors These control variables have upper and lower limits, respectively. By changing the settings, system voltage, generator reactive power and system loss can be changed. This point can be rephrased as follows. That is,
Setting a control variable that minimizes system loss while satisfying system behavior constraints and control variable constraints is an appropriate reactive power plan.

In other words, generator voltage transformer tap reactive power source (injection: power capacitor, absorption: shunt reactor), PV bus voltage value constraint reactive power value constraint active power constraint transformer tap constraint It is required to control under each constraint of (1) to minimize the cost of reactive power source and the loss of transmission power.

A sensitivity analysis, which is a conventional method for reactive power planning, will be described below. The sensitivity indicates how much the optimum solution responds to changes in environmental conditions. Therefore, according to the sensitivity analysis, it is possible to analyze the optimum solution for a part of the change in the environmental conditions. For issues related to reactive power allocation:
State and control vectors (variables) are determined.

State vector

[Equation 1]

Control vector

[Equation 2]

In the equations (8) and (9), Q _G is the active power of the generator, V _pq is the voltage value of the PQ bus, Q _f is the reactive power of the branch, V _pq is the PV bus, and T _p is the load. The transformer tap position, Q _s, is the new reactive power supply. Using the state and control vectors, the problem with reactive power allocation is expressed as: [ΔX] = [S] [ΔU] (10)

[0018]

(Equation 3)

Here, S is a sensitivity vector of the state vector ΔX with respect to the control vector ΔU, and is an inverse matrix of the Jacobian matrix used in the Newton-Raphson method.
The total transmission loss P _L is expressed as follows using the above definition.

[0020]

[Equation 4]

The constraint condition is

(Equation 5)

Here, n is the total number of buses. 1 to m is P
V bus is shown, and others are PQ bus (load bus).

The objective function for reactive power distribution is shown below. Minimizing the objective function for active power loss is expressed as:

[0024]

(Equation 6)

Here, nl is the number of transmission lines, V _i and V _j are voltage values at nodes i and j, θ _i and θ _j are nodes i and j.
, G _ij ^k represents the conductance of the transmission line k between the nodes i and j. Minimizing reactive power construction costs is expressed as follows.

[0026]

(Equation 7)

Here, np is the total installable number of reactive power devices, Z _j is the size of each reactive power device, C _p ^v _(Zj) is the variable cost of each reactive power device, and C _p ^f _(Zj) is each It is a fixed cost of the reactive power device. Minimizing voltage fluctuations is expressed as:

[0028]

(Equation 8)

Where nb is the total number of busbars in the system, V
_L is the voltage value at the node L, _VL ^spec is the voltage setting value at the node L, and ΔV _L ^max is the maximum voltage fluctuation allowable value. Minimizing the fluctuation of the tidal current is expressed as follows.

[0030]

[Equation 9]

Where P _m is the power flow of the transmission line m, P _m ^spec
Is the power flow setting value of the transmission line m, and ΔP _m ^max is the maximum value of the power flow fluctuation allowable value.

The constraint conditions are shown below. The constraint condition regarding the load is expressed as follows. CS ₁ ⇒ F (X, U, Z) = 0 (18) where X is the state vector, U is the control vector, and Z is the reactive power device position.

The constraint condition regarding the operation is expressed as follows. CS ₂ ⇒V _i ^min ≦ V _i ≦ V _i ^max (19) Here, V _i , V _i ^min , and V _i ^max are designated voltage values, respectively.
The minimum voltage value and the maximum voltage value. ^{_{CS 3 ⇒P gi min ≦ P gi}} ≦ P gi max ··· (20) ^{_{where, P gi, P gi min,}} P gi max specified each power generation amount, the minimum amount of power generation, the maximum power generation amount. CS ₄ in ^{_{⇒Q gi min ≦ Q gi ≦ Q}} gi max ··· (21) ^{_{wherein, Q gi, Q gi min,}} Q gi max specified each reactive energy, minimum reactive energy, is the maximum amount of reactive power . ^{_{CS 5 ⇒T pi min ≦ T pi}} ≦ T pi max ··· (22) ^{_{where, T pi, T pi min,}} T pi max each specified number of taps, the minimum tap number, the maximum number of taps. CS ₆ ⇒ 0 ≦ Z _L ≦ Z _L ^max (23) where Z _L and Z _L ^max are the number of designated reactive power devices, respectively.
The maximum number of reactive power devices.

The constraint conditions relating to the stability of the power system and an unexpected situation such as a supply failure in the event of an accident are generally expressed as follows. CS ₈ ⇒ F _k (X _k , U _k , Z _k ) = 0 (24) CS ₉ ⇒ G _k (X _k , U _k , Z _k ) ≦ 0 (25)

From the above equations, the objective function TOF and the constraint condition TCS of the entire system are expressed as follows. TOF = OF ₁ + OF ₂ + OF ₃ + OF ₄ ... (26) TCS = CS ₁ + CS ₂ + ... + CS ₉ ... (27)

Various methods have been devised as a method for obtaining a variable that minimizes the objective function using the sensitivity relationship and the gradient method. For example, there are the following methods.

(1) Optimize active power and reactive power using the relationship between power flow sensitivity and cost sensitivity by linear programming (IEEE Transactions on Power Apparat
us and Systems, Volume 87, 1968, pp1687-1696, Pesch
on etc.). (2) The allocation of active power and the allocation of reactive power are adjusted to minimize the manufacturing cost. Active power is distributed by the Lagrangian multiplier method, and reactive power is allocated by the gradient method (Proceedings of IEEE, 1968, pp.
1877-1885, Dopazo, etc.). (3) Control the system voltage and reactive power distribution using the sensitivity relationship between the controlled variable and the controllable variable and the loss sensitivity index. Using Direct Search Method to Minimize System Loss (IEEE Transactions on Power Apparat
us and Systems, 1969, pp1544-1558, Hano et al.). (4) Minimize system loss by strictly performing reactive power injection and transformer tap switching. that time,
Converge the solution to the optimal point using an appropriate algorithm (IEEE Transactions on Power Apparatus and System
s, Vol. 87, 1968, pp40-48, Peschon et al.). (5) Use a non-linear optimization technique to determine the optimal power flow. In doing so, the Kuhn-Tucker theorem is used to minimize the objective function of manufacturing cost or loss (IEEE Transactions on Power Apparatus and Sys
tems, Vol. 87, 1968, pp1866-1876, Dommel et al.).

(6) Determine the loss sensitivity index, reactive power transfer index and steady state index. Use an appropriate search method to converge to the optimum system state (IEEE Transacti
ons onPower Apparatus and Systems, Volume 95, 1976
Year, pp1413-1421, Savulescu, etc.). (7) Using an optimization technique called a method of box that minimizes voltage fluctuation from a desired voltage,
Sensitivity analysis for power system (IEEE Transactions
on Power Apparatus and Systems, Volume 90, 1971, pp
2495-2508 by Narita et al.). (8) Adjust the transformer tap and generator terminal voltage so that the power flow in the line and the load bus is restored to the desired value (IEEE Transactions on Power Apparatus and System).
s, Vol. 95, 1976, pp325-334, Shoults, etc.). (9) A network including reactive power control as a constraint is determined. Then, linear programming is used to distribute the reactive power. At that time, weak restrictions are applied to the generator voltage, transformer tap, and generator reactive voltage (Paper, F79 214-8, IEEE Power ENgineering Society).
Winter Meeting, 1979, Hobson, etc.). (10) Reduce losses by scheduling voltages in the power grid. Minimize system loss by cooperating the transformer tap and generator voltage (Paper,
1978, American Power Conference).

FIG. 76 is a flowchart showing an example of a conventional reactive power plan creating method. This method uses the sensitivity relationship of power loss as a function of active power and reactive power at each node. First, a basic flow analysis is performed (step ST71). Then, the load bus, the reactive power of the generator, and the system loss are checked (step ST72). As a result of the check, if it is determined that the reactive power optimization control is necessary, the power loss sensitivity is calculated (steps ST73 and ST74). That is, the partial derivative of the power loss with respect to the active power and the partial derivative with respect to the reactive power are calculated. Further, inequalities of the dependent variable and the control variable are set (step ST75). Dependent variables are generator reactive power and load bus voltage, and control variables are generator voltage, transformer tap position and other reactive power source values.

Next, the linear programming method is executed to determine the value of the control variable (step ST76). Then, the system state is changed using those control variables (step ST7).
7). The power flow is calculated based on the state (step ST78), and the system state is checked again.

Each of the optimization methods described above depends on the type of problem to be solved. Problems to be solved include, for example, distribution of active power, distribution of reactive power, analysis of unexpected situations, and problems including all of them.
Further, as described above, the constraint condition is an element that is always used in each stage of the process of processing the optimization problem. Various constraints must be satisfied at each stage of optimization. That is, the constraints must always be considered at each stage.

However, in the reactive power planning, since the power system itself is non-linear and the tap value, the input amount of the power capacitor, and the like are integer values, a non-linear 0-1 mixing problem occurs, which is a global problem. Not just an optimal solution,
There may be many local optimal solutions. Since Lagrange's undetermined multiplier method or gradient is used to optimize such problems, it converges to one of the local solutions and does not necessarily converge to the global optimal solution when the search space is wide. Absent. That is, as an optimization method, although the solution converges and the solution is obtained, there is a problem that the solution is not necessarily the optimal solution because it is a local solution.

[0043]

Since the conventional method for creating a reactive power plan of a power system is performed as described above, there is a problem that an optimal distribution of reactive power is not always realized. In addition, in the conventional method, the constraint conditions must be taken into consideration at each stage in the process of obtaining the optimum solution, so it takes time to reach the optimum solution, and it is difficult to realize a quick reactive power plan. There were also problems. Furthermore, since a method that depends on problems such as distribution of active power, distribution of reactive power, and analysis of an unexpected situation is adopted, there is a problem that it is lacking in versatility.

The present invention has been made to solve the above-mentioned problems, and can realize reactive power control closer to an optimal solution, and can quickly eliminate reactive power without the need to always consider constraint conditions. The purpose is to obtain a reactive power plan creation method for a power system that can be planned.

[0045]

According to a first aspect of the present invention, there is provided a reactive power planning method for a power system, which identifies a control variable in the power system and generates a chromosome in a genetic algorithm having the control variable as a gene. Then, the generated chromosome is genetically manipulated, and the value of the reactive power source indicated by the gene on each chromosome subjected to the genetic manipulation is included in the objective function for transmission loss in the power system and the objective function for total voltage fluctuation. It is evaluated by the total objective function.

According to a second aspect of the present invention, in a reactive power plan creating method for a power system, a plurality of power systems including a main system including a backbone node and a node other than the backbone system are provided before identification of control variables. It is to be divided into a subsystem and.

In the method for creating a reactive power plan of a power system according to the third aspect of the present invention, when performing a genetic operation, the subsystem is regarded as a node having a fixed power generation amount and a load, and the operation is performed on the main system. Is to do.

In the method for creating a reactive power plan of an electric power system according to a fourth aspect of the present invention, when performing a genetic operation, the main system is regarded as a node having a fixed amount of power generation and a load, and each subsystem is The operation is performed.

In the method for creating a reactive power plan of the electric power system according to the fifth aspect of the present invention, the generator voltage in the system, the transformer tap position, and the power capacitor input amount are used as control variables.

According to the sixth aspect of the present invention, there is provided a method for creating a reactive power plan for a power system, in which a chromosome is generated with a value satisfying an upper limit value and a lower limit value of a control variable as a gene.

According to the seventh aspect of the present invention, there is provided a method for creating a reactive power plan of a power system, wherein a linear combination of weighted objective functions is created as a fitness function used in the next genetic operation.

According to the eighth aspect of the present invention, the method for creating a reactive power plan of a power system creates a fitness function using the correlation of each objective function as a weight.

[0053]

The operation steps in the invention according to claim 1 are:
For each chromosome, a genetic operation by selection, crossover, and mutation is executed, and in the evaluation step, the power flow calculation is performed using the value indicated by the gene on each chromosome subjected to the genetic operation, and the objective function based on the calculation result is calculated. Create and rate it.

In the dividing step according to the second aspect of the invention, the power system is divided into a plurality of blocks, for example, 500k.
A main system focusing on the node related to the upper voltage of V and each subsystem related to the node related to the lower voltage of 275 kV or less in each block are generated.

In the operation step according to the third aspect of the present invention, a genetic operation is performed on each chromosome in which the control variable in the main system is gene-ized. Then, in the evaluation step, the power flow calculation is performed using the value indicated by the gene in each chromosome subjected to the genetic manipulation in the main system, and the objective function based on the calculation result is created and evaluated.

In the operation step according to the fourth aspect of the present invention, a genetic operation is also performed on each chromosome in which the control variable in each subsystem is gene-ized. Then, in the evaluation step, the power flow calculation is performed using the value indicated by the gene on each chromosome subjected to the genetic manipulation in each subsystem, and the objective function based on the calculation result is created and evaluated.

In the generating step according to the fifth aspect of the invention, a chromosome having genes as generator voltage, transformer tap position and power capacitor input amount in the system is generated. The operation step outputs a generator voltage, a transformer tap position, and a power capacitor input amount that have higher fitness as a result of the genetic operation.

In the generating step according to the sixth aspect of the invention, each control variable within the range of the constraint condition is generated to generate a chromosome having the gene as a gene.

In the fitness producing step according to the seventh aspect of the present invention, as the fitness function used in the next genetic operation, an objective function created based on each control variable resulting from the genetic operation is used. To create.

In the fitness generating step according to the eighth aspect of the present invention, when the fitness function is created, the correlation of each objective function is used as a weight, and each objective function influences the fitness function evenly. To do so.

[0061]

【Example】

Example 1. The reactive power planning method of the power system using the genetic algorithm is composed of the following five stages. (1) Analyze power system data. That is, it identifies the number of system busbars, power lines, transformers, capacitors / reactors and generators. In addition, the number of control targets that affect the reactive power flow is identified. Controlled objects include generator voltage, transformer taps and power capacitors.
Note that the shunt reactor can be treated as a negative power capacitor.

(2) As shown in FIG. 1, chromosomes 1 to i are generated. One row shown in FIG. 1 will be called one individual. Further, each column corresponds to each generator voltage, each transformer tap, and each power capacitor.
Several normalized solution candidates corresponding to each column are randomly generated, binarized and connected to each column. FIG.
In, P _{n11 to} P _n1k are k normalized values corresponding to the individual P ₁ . Similarly, P _{n21 to} P _n2l are l normalized values corresponding to the individual P ₂ , and P _nn1 to P _n1
P _nnp is p normalized values corresponding to the individual P _n . Each normalized value is coded and then randomly concatenated into a corresponding column, as shown in FIG. As described above, each individual having each chromosome is generated. Each column in each individual corresponds to each generator voltage, each transformer tap, and each power capacitor. In addition, the fitness is given to each chromosome.

(3) Genetic manipulation is performed on the generated chromosome. Genetic manipulation consists of selection, crossover and mutation. The selection is to determine which two of the chromosomes are crossed over according to the fitness. For example,
Selection is performed based on a model called a roulette model. Individuals with a high degree of fitness are selected with high probability, so that offspring can be left with high probability. Crossover is an operation that recombines two parent chromosomes to generate a child chromosome. The mutation is an operation of accidentally changing a certain bit in the bit string of the chromosome from "0" to "1" or from "1" to "0".

(4) Power flow analysis is performed on the control variables determined by the genetic manipulation. That is, the voltage value and the phase angle at each node are calculated, and the power flow, transmission loss, changed bus power and total active power loss are determined.

(5) To evaluate the genetic manipulation. That is, the objective function regarding the total active power loss and the total voltage fluctuation is calculated. Also, a fitness function in which a linear weighting is added to the objective function is created.

(6) Then, until a large change does not occur in the objective function and all the chromosomes converge, the above (3) to
Repeat (5).

A method of determining various quantities of reactive power devices in the power system plan will be described below. Figure 2 is IEEE
It is a system system diagram which shows a 30 standard bus system. In the figure, 101 to 130 are bus bars, 201 to 241 are power transmission lines, T1 to T4 are transformers, C1 and C2 are power capacitors, and G1 to G6 are generators. Here, taking the IEEE30 bus system shown in FIG. 2 as an example, the number of buses m =
30, the number of transmission lines is l = 41, the number of transformers is t = 4, the number of capacitors is c = 2, and the number of generators is g = 6. That is, in the analysis of power system data, each number is identified as described above. As control variables, the number of controlled devices is t = 4 transformers, c = 2 capacitors, and g = 6 generators.

FIG. 3 shows transmission line data in the IEEE30 bus system. In the figure, the vertical direction indicates each transmission line, and the horizontal direction indicates the transmission line number, the bus bar at one end of the transmission line, the bus bar at the other end, the resistance value, the reactance value, the capacitance / 2, and the normalized tap position. . FIG.
Shows bus data in the IEEE30 bus system. In the figure, the vertical direction indicates each bus bar, and the horizontal direction indicates the bus number, voltage value, its lower limit, its upper limit, power generation amount (effective portion), power generation amount (ineffective portion), load (effective portion), load. (Ineffective amount), the lower limit of the power generation amount (ineffective amount), the upper limit of the power generation amount (ineffective amount), and the input capacitor amount.

Next, the operation will be described with reference to the flowchart of FIG. The distribution of reactive power is determined by the control variable. That is, it depends on the transformer tap position, the generator voltage, and the manipulated variable of the reactive power source. The reactive power of the generator and the voltage on the load bus are ancillary controlled variables. Therefore, the reactive power plan defines the value of the control variable that minimizes the system loss while satisfying the constraint condition of the behavior of the network and the constraint condition for the control variable. In other words, the value of the control variable is determined so as to minimize active power loss and voltage fluctuation. That is, the genes of the chromosome in the genetic manipulation may be made to correspond to those control variables.

In this case, the length of the chromosome in the genetic manipulation is 4 + 2 + 6 = 12. That is, one chromosome is composed of 12 genes. String length of 1 gene is 1
Assuming 0 bits, one chromosome is represented by 120 bits. The number of individuals is 50 or 100, and the crossover probability is 0.
A simulation of 400 generations is performed with a mutation probability of 08 and a mutation probability of 0.003.

In the generation of chromosomes, first, normalization is performed (step ST1). As described above, each value corresponding to the control variable is converted into a standardized value by a predetermined calculation. FIG. 6 is an explanatory diagram for explaining the normalization of the chromosome of one individual. As shown in FIG. 6, the generator voltage GV _i is normalized as follows. GV _i ⁿ = [(GV _i −GV _i ^min ) / (GV _i ^max −GV _i ^min )] · 2 ^ls (28) where GV _i ^min is the amount of power that can be generated by the i-th generator. , GV _i ^max is the upper limit of the amount of power that can be generated, and ls is the string length of the gene.

The transformer tap position is normalized as follows. TP _i ⁿ = [(TP _i −TP _i ^min ) / (TP _i ^max −TP _i ^min )] · 2 ^ls (29) Here, TP _i ^min is the lower limit of tap switching of the i-th transformer. The value, TP _i ^max, is the upper limit of tap switching of the transformer.

The input amount of the power capacitor is normalized as follows. SC _i ⁿ = [(SC _i −SC _i ^min ) / (SC _i ^max −SC _i ^min )] · 2 ^ls (30) Here, SC _i ^min is the input amount of the i-th power capacitor. The lower limit value, SC _i ^max, is the upper limit value of the input amount of the power capacitor.

As is clear from the expressions (28) to (30), the inequality constraint condition is added here. Therefore, it becomes unnecessary to consider the inequality constraint condition thereafter.
Further, in each formula, the value in [] is a value between 0 and 1, and the control variable is equivalent to being normalized to a value between 0 and 1.

Then, the decimal value obtained by the normalization is encoded into binary data having a predetermined string length (step ST2). Furthermore, the coded values are concatenated to form a chromosome having each value as a gene (step S
T3). As described above, the chromosome for one individual is generated. The above process is performed for other values of generator voltage, transformer tap position and power capacitor input to generate some chromosomes. Here, 50 or 100 chromosomes are generated.

Next, a genetic operation is performed (step ST
4). In the genetic operation, first, a selection operation for randomly selecting individuals so that individuals with high fitness can leave their offspring is performed. Then, a crossover process of mating the two selected individuals is performed. Genes are recombined in the crossover process. Furthermore, mutation processing is performed to change the gene with a certain probability. Mutations allow for faster and more accurate convergence.

When the genetic operation is completed, the chromosomes are separated and decoded (step ST5). That is, each gene constituting the chromosome is separated. Each gene corresponds to a normalized and coded control variable. Further, each separated gene is decoded into a decimal number. Then, an inverse normalization operation is performed (step ST
6). FIG. 7 is an explanatory diagram for explaining the denormalization of the chromosome of one individual. As shown in FIG. 7, the generator voltage GV _i is denormalized as follows. GV _i = GV _i ⁿ · [(GV _i ^max −GV _i ^min ) / 2 ^ls ] + GV _i ^min (31)

The transformer tap position is denormalized as follows. ^{_{TP i = TP i n · [}} (TP i max -TP i min) / 2 ls] + TP i min ··· (32) power capacitors input amount is reverse normalized as follows. SC _i = SC _i ⁿ · [(SC _i ^max −SC _i ^min ) / 2 ^ls ] + SC _i ^min (33)

The change of the control variable based on the result of the genetic manipulation as described above is mapped to the power system. That is, of the control variables, the transformer tap position and the power capacitor input amount are reflected in the bus admittance matrix. The generator voltage is directly input to the power flow calculation unit.
The power flow calculator calculates voltage values and phase angles at all nodes in the system. Further, using the admittance matrix, the voltage value of the node, and the phase angle, the power flow and the transmission loss between the nodes are calculated by the fast Newton-Raphson method or the like (step ST7). Furthermore, the slack bus power and the total active power loss of the system are calculated.

Next, each objective function is created. The objective functions relate to the total active power loss in the system and the voltage fluctuations of the node whose voltage value is outside the upper or lower limit. Further, the voltage values of those nodes are calculated. Then, a total objective function corresponding to the sum of the objective functions is created. In addition, a fitness function, in which each objective function is weighted, is created.
The fitness function is used in the selection operation in the next genetic operation.

FIG. 8 is a flow chart for explaining the process of creating active power loss, voltage fluctuation, total objective function, and fitness function. As shown in the figure, the total active power loss P _L is expressed as follows. ^{_{P L k = Σ l = 1}} n l G ij l [V i 2 + V j 2 -2V i V j cos (θ i -θ j) ··· (34) where, nl is the number of transmission lines, G _ij ^l is the conductance of the transmission line 1 between the nodes i and j, V _i and V _j are the voltages at the nodes i and j, and θ _i and θ _j are the phase angles at the nodes i and j. The total voltage fluctuation TDV ^k is expressed as follows. TDV ^k = Σ _{i = 1} ^N ΔV _i ^k (35) Note that N is a bus whose voltage exceeds the limit value, and ΔV
_i ^k is given by ΔV _i ^k = W _i ^k | V _i ^k −V _i ^o |. The next V _i ^{k + 1} in the iterative calculation is V _i ^{k + 1}
= V _i ^k ± α (ΔV _i ^k ). α is a proportional constant that is approximately 1 (generally about 1.1). As V _i ^k , k
The voltage value in the second iteration may be used, or the lower limit or the upper limit may be used. The upper limit value and the lower limit value, which are constraint conditions, are used to prevent the voltage value during calculation from exceeding a predetermined range.

The total objective function is given by TOF ^k = P _L ^k + W _o ^k TDV ^k (36). Here, W _o ^k is a weighting coefficient for normalizing the two objective functions, and is given by P _L ^av / TVD ^av . Here, P _L ^av is an average of active power loss in all individuals, and TVD ^av is an average of total voltage fluctuations in all individuals.

The fitness function is given by weighting the inversion of the objective function as follows. FT ^k = (1 / P _L ^k ) + W _f ^k · (1 / TDV ^k ) ... (37) Here, W _f ^k is given by TVD ^av / P _L ^av . Therefore, the fitness function having the largest value is given to the chromosome to which the objective function having the smallest value is given. Also, at each genetic operation stage, the two objective functions P _L ^k and TDV ^k have relatively different values. Therefore, the weight W _f ^k is appropriately normalized so that the fitness function gives a good fitness in the selection process of the genetic operation.

Then, it is judged whether or not there is a significant change between the objective function and the objective function by the previous operation (step ST9). If there is a change, perform the following genetic manipulation. If there is no significant change and it is determined that all chromosomes have converged, the processing ends.

9 and 10 show 10 when the number of individuals is 50.
It shows the change in the total active power loss in the case of zero.
The horizontal axis represents the number of generations, that is, the number of times of genetic manipulation. As shown in both figures, when the number of individuals is 50,
Although the convergence value of active power loss is slightly large, it converges quickly. 11 and 12 show 1 when the number of individuals is 50.
It shows the change in the total voltage fluctuation in the case of 00. The horizontal axis indicates the number of generations. When the number of individuals is 50, it converges quickly, but when the number of individuals is 100, the degree of improvement is higher. FIG.
3, FIG. 14 shows changes in the total objective function when the number of individuals is 50 and 100. The horizontal axis indicates the number of generations. When the number of individuals is 50, it converges quickly, but when the number of individuals is 100, the degree of decrease of the objective function is higher.

FIG. 15 and FIG. 16 show 1 when the number of individuals is 50.
It shows the degree of convergence of the genetic operation in the case of 00.
The horizontal axis indicates the number of generations. It can be seen that when the number of individuals is 50, it converges quickly. 17 and 18 have five individuals.
In the case of 0, the change of the slack bus active power in the case of 100 is shown. The horizontal axis indicates the number of generations. When the number of individuals is 50, it converges faster, but when the number of individuals is 100, the convergence value is smaller. FIG. 19 and FIG. 20 show changes in the slack bus reactive power when the number of individuals is 50 and 100, respectively. The horizontal axis indicates the number of generations. After all, when the number of individuals is 50, it converges quickly, but when the number of individuals is 100, the convergence value is smaller. Figure 21 shows the case of 50 individuals and 100
It is a figure showing a comparison result of slack bus power and power loss between the case of an individual. In the figure, the vertical direction indicates the initial state, the case of 50 individuals, the case of 100 individuals, and the horizontal direction indicates the slack bus power and transmission loss. Excluding the reactive power loss, the slack bus voltage and the power loss are smaller when the number of individuals is 100.

Each of FIGS. 22 to 27 has 100 individuals.
It shows the change in the generated voltage of the generators G1 to G6 in the case of. The horizontal axis indicates the number of generations. FIG. 28 shows the converged values of the generator voltage by the genetic operation in the case of 50 individuals and 100 individuals. In the figure, the vertical direction indicates the generator, and the horizontal direction indicates the generator number, the bus number, the lower limit value of the generator voltage, the initial value, the convergence value for 50 individuals, the convergence value for 100 individuals, respectively. Indicates the upper limit of generator voltage. 22 to 28, each generator G1
It can be seen that the generated voltages of G6 to G6 converge within the upper and lower limits, respectively.

29 to 32, the number of individuals is 100, respectively.
The change of the tap position of transformers T1-T4 in the case of is shown. The horizontal axis indicates the number of generations. FIG. 33 shows 50
It shows the convergence value of the transformer tap position by the genetic operation in the case of 100 individuals. In the figure, the vertical direction indicates the transformer, and the horizontal direction indicates the transformer number, the transmission line number, one bus number, the other bus number, the lower limit value of the transformer tap position, the initial value, and 50 individuals, respectively. Shows the convergence value of, the convergence value for 100 individuals, and the upper limit value. From FIGS. 29 to 33, it can be seen that the tap positions of the transformers T1 to T4 converge within the upper and lower limits, respectively.

34 and 35, the number of individuals is 100, respectively.
In this case, the change in the input amount of the power capacitors C1 and C2 is shown. The horizontal axis indicates the number of generations. Fig. 36
Shows the convergence value of the input amount of the power capacitor by the genetic operation in the case of 50 individuals and 100 individuals. In the figure, the vertical direction indicates capacitors, and the horizontal direction indicates the node number, the lower limit value of the power capacitor input amount, the initial value, the convergence value for 50 individuals, the convergence value for 100 individuals, and the upper limit value, respectively. Show. 34 to 36, it can be seen that the input amounts of the power capacitors C1 and C2 converge within the upper and lower limits, respectively.

Each of FIGS. 37 to 60 has 100 individuals.
In the case of No. 3,4,6,7,9,10,12,14
˜30 busbars (busbars 103, 104, 10 in FIG. 2)
6,107,109,110,112,114-13
It shows the change of the bus voltage of 0). The horizontal axis indicates the number of generations. 61 shows No. 3 shows busbar voltage amounts after 400 generations of genetic manipulation of busbars 1 to 30 (buslines 101 to 130 in FIG. 2). In the figure, the vertical direction indicates each busbar, and the horizontal direction indicates the busbar number,
The bus bar type, the lower limit, the initial value, the voltage value when the number of individuals is 50, the voltage value when the number of individuals is 100, and the upper limit are shown. FIG. 37
From FIG. 61, it can be seen that each bus voltage converges within the upper and lower limits while gradually narrowing the width of vibration.

As described above, by repeating the genetic operation so as to reduce the active power loss and the voltage fluctuation, the optimum values of the generator voltage, the transformer tap position and the reactive power device input amount in the power system are determined. can do. At that time, since the constraint condition is reflected in the gene, it is not necessary to consider the constraint condition when evaluating the objective function and the fitness function. Therefore, the optimal solution can be easily reached regardless of the constraint conditions.
In the conventional method, it is necessary to consider the constraint condition at each step of the optimization process. But in this case,
The constraint condition is considered only when the voltage values at the first stage of executing the genetic algorithm and the stage of calculating the objective function are within a predetermined range. Further, when evaluating the objective function and the fitness function, the total objective function is used for the evaluation, so that the optimum solution can be easily reached regardless of the objective function. Furthermore, unlike the conventional method that uses one point as a search point,
Since the individuals corresponding to a plurality of search points are used for the search, the risk of falling into a local solution is avoided.

Example 2. Next, an example in which the genetic algorithm is applied to a larger power system will be described.
FIG. 62 is a system configuration diagram showing a large-scale power system.
In this system, power is transmitted at various voltage levels. That is, 1) 500 kV, 2) 275-1
Power is transmitted at each level of 85 kV, 3) 154 to 110 kV, and 4) less than 110 kV. Note that this power system is a radial system.

In this embodiment, the power system is divided into a plurality of blocks based on the power transmission level of 500 kV. Each block contains some 275 kV or less nodes. The power system shown in FIG. 62 is a 500 kV bus 1001
1021 and 500 kV transmission lines 2001-2021
But includes eight blocks 501, as shown in FIG.
˜508. FIG. 64 shows the configuration shown in FIG. 63 focusing on the 500 kV level. The position and load of the power capacitors are also shown schematically in FIG. That is, in FIG. 64, 611 to 68
1 is a 500 kV generator. Also, 710 to 780
Is a power capacitor on a 500 kV bus. Therefore, for example, the configuration of the block 501 shown in FIG.
As shown in FIG. 64, the main node 1020, the generator 611.
And a power capacitor 710. Eight blocks 501 to 50 having the configuration shown in FIG.
The configuration including 8 will be called a main system.

Further, FIGS. 65 to 72 show the configuration shown in FIG. 63 in more detail for each block 501 to 508. That is, a portion including a transmission line of 275 kV or less, a bus bar, a generator, and the like is shown. Also, power capacitors 711, 712, 722-
724, 731 to 734, 741 to 744, 751 to 7
56, 761-763, 771, 772, 782, 78
2 is also specified.

The main system includes 19 generators 61.
1-681 and 8 power capacitors 710-78
19 control variables corresponding to 0 are included. Then, the genetic operation is repeated for this main system to obtain the optimum value of the control variable. Then, the genetic operation is repeated for each of the blocks 501 to 508 shown in FIGS. 65 to 72 to obtain the optimum value of the control variable. Therefore, in this embodiment, attention is paid to the 500 kV level, a main system of the power system is created, and a genetic operation is performed on the main system, and then a genetic operation is performed on the detailed configuration of each block constituting the main system. That is, in this embodiment,
Hierarchical processing is performed. In addition, hereinafter, FIGS.
5 of the configurations of the blocks 501 to 508 shown in FIG.
The parts other than the 00 kV level are called subsystems.

Next, the operation will be described with reference to the flow charts of FIGS. FIG. 73 is a flowchart showing the entire process, FIG. 74 is a flowchart showing the process for the main system in detail, and FIG. 75 is a flowchart showing the process for the subsystem in detail.

First, the processing relating to the main system will be described. This process determines the reactive power source at the main node so as to minimize active power loss and voltage fluctuations. The subsystem is considered to be a node having a fixed amount of power generation and a load during execution of processing related to the main system. In the actual processing, first, the main system and the subsystem are determined (step ST3).
1). As shown in FIG. 64, the main system has 19
There are 8 generators and 8 power capacitors. That is, 27 control variables are identified (step ST3).
2, ST51). Note that there is no transformer here. Therefore, it is determined to generate a chromosome having 27 genes (step ST52). Therefore, if the gene string length is 5 bits, a chromosome consisting of 135-bit data is generated. If the gene string length is 10 bits, a chromosome consisting of 270-bit data is generated. The gene value of each chromosome is randomly determined (step ST53).

Then, the genetic operation as described above is performed (steps ST33, ST54). The decoding process is performed for each chromosome after the genetic operation is completed, and the power flow calculation is performed as described above (step ST
34, ST55). Then, the total active power loss and the total voltage fluctuation are calculated based on the result of the power flow calculation, and the total objective function is determined from the calculation result (step ST5).
5). When the genetic operation converges, that is, when the change in the total objective function disappears and becomes minimum,
The process for the main system ends. If it does not converge, the genetic operation is further continued (step ST
35, ST56).

When converged, the voltage value of the main node and the control amount of the reactive power device (here, the power capacitor) are determined (step ST57). Therefore, the processing for the subsystem is performed by using them as fixed specific values (steps ST36 and ST58). Since the power system is radial and each subsystem can be treated as an independent block, genetic operations can be performed independently. From the perspective of the main system, each subsystem can be regarded as an external system. During the processing of the subsystem, the main system is regarded as a node having a fixed amount of power generation and a load.

Next, from step ST37 in FIG.
Operation of the subsystem shown in ST43
This will be described with reference to the flowchart in FIG. First, the number of buses, the number of power transmission lines, the number of generators, the number of tap switching transformers, and the number of power capacitors in each subsystem are identified (steps ST101, ST201, ST801). Then, each gene having a length corresponding to the number of generators, the number of transformers, and the number of power capacitors is generated (steps ST102, ST
202, ST802). Furthermore, in each subsystem, genetic operations for selection, crossover, and mutation for each chromosome are performed (steps ST102, ST202, S).
T802).

For each chromosome that has been genetically manipulated, the power flow calculation is performed as described above (step ST10).
3, ST203, ST803). Then, based on the calculation result, the total active power loss and the total voltage fluctuation are obtained, and the total objective function is created from them. Also, a fitness function is created (steps ST104, ST204, ST
804). Further, when the genetic operation has converged, that is, when the total objective function has changed and becomes minimum, the processing for the subsystem is terminated.
If it does not converge, the genetic operation is further continued (steps ST105, ST205, ST805). When converged, it means that the generator voltage, the transformer tap position, and the input amount of the power capacitor for the subsystem are determined (steps ST105, ST205, ST
805).

The generator voltage and the reactive power unit control amount for each subsystem are arranged and according to their values,
The control amount in the power system is updated (step ST4
3, ST106). In addition, when a sufficient improvement effect cannot be obtained by the above processing, the genetic processing may be further continued.

As described above, the time required to determine the control amount is shortened by dividing the 500 kV power transmission system into a plurality of blocks and dividing the main system for 500 kV and each subsystem for 275 kV or less into reactive power control. be able to. That is, the processing for the main system only needs to be executed once, and the parallel processing of the subsystems can be easily performed. When the main system is running, the subsystem part is regarded as a node having a fixed power generation and load, and conversely, when the subsystem is running, the main system part is regarded as a node having a fixed power generation and load. Therefore, when the subsystem is executed after the main system is executed, the power generation amount and the load value fixed at their joints may be inconsistent. In this case, it is necessary to repeatedly calculate as shown by the arrow from ST43 to ST33 in FIG.

[0104]

As described above, according to the invention described in claim 1, the method for creating a reactive power plan of a power system generates a chromosome in a genetic algorithm in which a control variable is a gene, and the generated chromosome is Since the value of the reactive power source indicated by the gene on each chromosome that has been genetically manipulated is evaluated using the total objective function, the conventional linear programming or nonlinear programming method is used. Compared with the case where there is a case, there is an effect that the possibility of falling into a local solution is reduced and the control amount of the reactive power device which is closer to the optimum solution or the optimum solution can be determined.

According to the second aspect of the present invention, a method for creating a reactive power plan of a power system divides the power system into a main system including a backbone node and a plurality of subsystems including nodes other than the backbone system. Therefore, the control amount of the reactive power device of the main system and the control amount of the reactive power device of each subsystem can be independently determined, and the reactive power planning can be performed with a smaller calculation amount than that for processing all the power grids. There is an effect that can be created.

According to the third aspect of the present invention, the method for creating a reactive power plan of a power system regards a subsystem as a node having a fixed amount of power generation and a load when performing a genetic operation, and the main system is considered. The control amount of the reactive power device of the main system can be determined independently of the subsystem.

According to the invention described in claim 4, in the reactive power plan creating method of the power system, when performing the genetic operation, the main system is regarded as a node having a fixed amount of power generation and a load, and each sub-system is considered. Since the system is operated, there is an effect that the control amount of the reactive power device of each subsystem can be determined independently of the main system. In addition, since the subsystems are independent of each other, the control amounts of the reactive power devices of all the subsystems can be determined at the same time, and the reactive power plan of the entire power system can be executed at high speed.

According to the fifth aspect of the invention, the method for creating a reactive power plan of the power system uses the generator voltage in the system, the transformer tap position, and the power capacitor input amount as control variables. Therefore, the genetic algorithm has the effect of determining the optimal or near-optimal control amount of each reactive power device without falling into a local solution.

According to the sixth aspect of the present invention, the method for creating a reactive power plan of the power system is designed to generate a chromosome with a value satisfying the constraint condition of the control variable as a gene. There is no need to consider the constraint conditions each time in the process, and there is an effect that the time until reaching an optimal solution or a solution close to the optimal solution can be shortened.

According to the seventh aspect of the present invention, the method for creating a reactive power plan of a power system creates a linear combination of weighted objective functions as a fitness function used in the next genetic operation. Therefore, in the next genetic operation, in the chromosome corresponding to the objective function with the smallest value,
The fitness function with the largest value is given, and it has the effect of speeding up the convergence of the genetic processing.

According to the eighth aspect of the invention, since the reactive power plan creating method of the power system creates the fitness function by using the correlation of each objective function as a weight, each objective function is Even if they take relatively different values, there is an effect that each objective function can evenly affect the selection process of the genetic operation. That is,
Each objective function always has the effect of effectively affecting the fitness function.

[Brief description of drawings]

FIG. 1 is an explanatory diagram showing a method of forming chromosomes in a genetic algorithm.

FIG. 2 is a system diagram showing an IEEE30 bus system.

FIG. 3 is an explanatory diagram showing transmission line data in the IEEE30 bus system.

FIG. 4 is an explanatory diagram showing bus bar data in the IEEE30 bus system.

FIG. 5 is a flowchart showing the operation of a reactive power plan creating method according to an embodiment of the present invention.

FIG. 6 is an explanatory diagram for explaining the normalization for one chromosome.

FIG. 7 is an explanatory diagram for explaining inverse normalization for one chromosome.

FIG. 8 is a flowchart for explaining a process of creating active power loss, voltage fluctuation, total objective function, and fitness function.

FIG. 9 is an explanatory diagram showing changes in total active power loss when the number of individuals is 50.

FIG. 10 is an explanatory diagram showing changes in total active power loss when the number of individuals is 100.

FIG. 11 is an explanatory diagram showing changes in total voltage fluctuation when the number of individuals is 50.

FIG. 12 is an explanatory diagram showing changes in total voltage fluctuation when the number of individuals is 100.

FIG. 13 is an explanatory diagram showing changes in the total objective function when the number of individuals is 50.

FIG. 14 is an explanatory diagram showing changes in the total objective function when the number of individuals is 100.

FIG. 15 is an explanatory diagram showing the degree of convergence of a genetic operation when the number of individuals is 50.

FIG. 16 is an explanatory diagram showing the degree of convergence of a genetic operation when the number of individuals is 100.

FIG. 17 is an explanatory diagram showing changes in the slack bus effective voltage when the number of individuals is 50.

FIG. 18 is an explanatory diagram showing changes in the slack bus effective voltage when the number of individuals is 100.

FIG. 19 is an explanatory diagram showing changes in the slack bus reactive voltage when the number of individuals is 50.

FIG. 20 is an explanatory diagram showing changes in the slack bus reactive voltage when the number of individuals is 1000.

FIG. 21 is an explanatory diagram showing a comparison result of slack bus power and power loss between the case of 50 individuals and the case of 100 individuals.

FIG. 22 is an explanatory diagram showing changes in the generated voltage of the generator G1 when the number of individuals is 100.

FIG. 23 is an explanatory diagram showing changes in the generated voltage of the generator G2 when the number of individuals is 100.

FIG. 24 is an explanatory diagram showing changes in the generated voltage of the generator G3 when the number of individuals is 100.

FIG. 25 is an explanatory diagram showing changes in the generated voltage of the generator G4 when the number of individuals is 100.

FIG. 26 is an explanatory diagram showing changes in the generated voltage of the generator G5 when the number of individuals is 100.

FIG. 27 is an explanatory diagram showing changes in the generated voltage of the generator G6 when the number of individuals is 100.

FIG. 28 is an explanatory diagram showing the convergence value of the generator voltage by the genetic operation in the case of 50 individuals and in the case of 100 individuals.

FIG. 29 is an explanatory diagram showing changes in the tap position of the transformer T1 when the number of individuals is 100.

FIG. 30 is an explanatory diagram showing changes in tap positions of the transformer T2 when the number of individuals is 100.

FIG. 31 is an explanatory diagram showing changes in the tap position of the transformer T3 when the number of individuals is 100.

FIG. 32 is an explanatory diagram showing changes in the tap position of the transformer T4 when the number of individuals is 100.

FIG. 33 is an explanatory diagram showing convergence values of transformer tap positions by genetic manipulation in the case of 50 individuals and in the case of 100 individuals.

FIG. 34 is an explanatory diagram showing changes in the input amount of the power capacitor C1 when the number of individuals is 100.

FIG. 35 is an explanatory diagram showing changes in the input amount of the power capacitor C2 when the number of individuals is 100.

FIG. 36 is an explanatory diagram showing the converged value of the input amount of the power capacitor by the genetic operation in the case of 50 individuals and 100 individuals.

FIG. 37 is an explanatory diagram showing a change in bus voltage of the bus bar 103 when the number of individuals is 100.

FIG. 38 is an explanatory diagram showing changes in the bus voltage of the bus bar 104 when the number of individuals is 100.

FIG. 39 is an explanatory diagram showing changes in the bus voltage of the bus 106 when the number of individuals is 100.

FIG. 40 is an explanatory diagram showing changes in the bus voltage of the bus bar 107 when the number of individuals is 100.

FIG. 41 is an explanatory diagram showing changes in bus voltage of the bus bar 109 when the number of individuals is 100.

FIG. 42 is an explanatory diagram showing changes in the bus voltage of the bus 110 when the number of individuals is 100.

FIG. 43 is an explanatory diagram showing changes in the bus voltage of the bus 112 when the number of individuals is 100.

FIG. 44 is an explanatory diagram showing changes in the bus voltage of the bus 114 when the number of individuals is 100.

FIG. 45 is an explanatory diagram showing changes in the bus voltage of the bus 115 when the number of individuals is 100.

FIG. 46 is an explanatory diagram showing changes in the bus voltage of the bus 116 when the number of individuals is 100.

FIG. 47 is an explanatory diagram showing a change in bus voltage of the bus 117 when the number of individuals is 100.

FIG. 48 is an explanatory diagram showing changes in the bus voltage of the bus 118 when the number of individuals is 100.

FIG. 49 is an explanatory diagram showing changes in the bus voltage of the bus 119 when the number of individuals is 100.

FIG. 50 is an explanatory diagram showing a change in bus voltage of the bus 120 when the number of individuals is 100.

FIG. 51 is an explanatory diagram showing changes in the bus voltage of the bus 121 when the number of individuals is 100.

FIG. 52 is an explanatory diagram showing changes in the bus voltage of the bus bar 122 when the number of individuals is 100.

FIG. 53 is an explanatory diagram showing a change in bus voltage of the bus bar 123 when the number of individuals is 100.

FIG. 54 is an explanatory diagram showing changes in the bus voltage of the bus 124 when the number of individuals is 100.

FIG. 55 is an explanatory diagram showing changes in the bus voltage of the bus 125 when the number of individuals is 100.

FIG. 56 is an explanatory diagram showing changes in the bus voltage of the bus 126 when the number of individuals is 100.

FIG. 57 is an explanatory diagram showing changes in bus voltage of the bus 127 when the number of individuals is 100.

FIG. 58 is an explanatory diagram showing a change in bus voltage of the bus bar 128 when the number of individuals is 100.

FIG. 59 is an explanatory diagram showing changes in the bus voltage of the bus 129 when the number of individuals is 100.

FIG. 60 is an explanatory diagram showing changes in the bus bar voltage of the bus bar 130 when the number of individuals is 100.

FIG. 61: 400 of genetic manipulations of buses 101-130
It is explanatory drawing which shows the busbar voltage amount after a generation.

FIG. 62 is a system configuration diagram showing a large-scale power system in another example of the present invention.

FIG. 63 is a block diagram showing each block resulting from division of a large-scale power system.

FIG. 64 is a system configuration diagram showing a main system.

FIG. 65 is a configuration diagram showing a detailed configuration of a block 501.

FIG. 66 is a configuration diagram showing a detailed configuration of a block 502.

67 is a configuration diagram showing a detailed configuration of a block 503. FIG.

68 is a configuration diagram showing a detailed configuration of a block 504. FIG.

69 is a configuration diagram showing a detailed configuration of a block 505. FIG.

70 is a configuration diagram showing a detailed configuration of a block 506. FIG.

71 is a configuration diagram showing a detailed configuration of a block 507. FIG.

72 is a configuration diagram showing a detailed configuration of a block 508. FIG.

FIG. 73 is a flowchart showing the overall processing of a reactive power plan creating method according to another embodiment of the present invention.

FIG. 74 is a flowchart showing the process of the main system.

FIG. 75 is a flowchart showing a process for a subsystem.

FIG. 76 is a flowchart showing an example of a conventional reactive power plan creation method.

[Explanation of symbols]

 1-i chromosome.

 ─────────────────────────────────────────────────── ─── Continuation of front page (72) Inventor Yoshio Izumi, 1-1-1, Tsukaguchihonmachi, Amagasaki City Mitsubishi Electric Corporation Industrial Systems Research Center

Claims (8)

[Claims] 1. A method for creating a reactive power plan of a power system for adjusting reactive power in a power system, comprising: an identifying step of identifying a control variable in the power system; and a chromosome in a genetic algorithm having the control variable as a gene. The generation step to generate, the operation step to perform the genetic operation on the generated chromosome, and the value of the reactive power source indicated by the gene on each chromosome subjected to the genetic operation are calculated as the objective function and the total power loss in the power system. And a step of evaluating by a total objective function including an objective function relating to voltage fluctuations. 2. The power system according to claim 1, further comprising a dividing step of dividing the power system into a main system including a node in a backbone system and a plurality of subsystems including nodes other than the backbone system before executing the identifying step. Reactive Power Plan Creation Method. 3. The operation step includes the step of performing genetic operation on the main system by regarding the subsystem as a node having a fixed amount of power generation and a load.
Method for creating a reactive power plan for the power system described. 4. The reactive power plan creation for a power system according to claim 3, wherein the operation step includes a step of treating the main system as a node having a fixed amount of power generation and a load and performing a genetic operation for each subsystem. Method. 5. The reactive power of a power system according to claim 1, wherein the generating step uses, as control variables, a generator voltage in the system, a transformer tap position, and a power capacitor input amount. How to make a plan. 6. The method for creating a reactive power plan of a power system according to claim 5, wherein the generating step generates a chromosome with a value satisfying an upper limit value and a lower limit value of the control variable as a gene. 7. The reactive power plan creating method for a power system according to claim 6, further comprising a fitness creating step of creating a linear combination of the weighted objective functions as a fitness function used in the next genetic operation. . 8. The method for creating a reactive power plan of a power system according to claim 7, wherein the adaptability creating step creates the adaptability function by weighting the correlation of each objective function.